- Email: [email protected]
Dynamic picking system for 3D seismic data: Design and evaluation
ARTICLE IN PRESS
Int. J. Human-Computer Studies 67 (2009) 551–560 www.elsevier.com/locate/ijhcs
Dynamic picking system for 3D seismic data: Design and evaluation Pierre Salom, Remi Megret, Marc Donias, Yannick Berthoumieu LAPS, IMS, CNRS UMR 5218, Universite´ Bordeaux 1, 351 cours de la Libe´ration, 33405 Talence, France Received 16 August 2007; received in revised form 23 December 2008; accepted 23 January 2009 Communicated by J. Stasko Available online 11 March 2009
Abstract In the framework of data interpretation for petroleum exploration, this paper contributes two contributions for visual exploration aiming to manually segment surfaces embedded in volumetric data. Resulting from a user-centered design approach, the first contribution, dynamic picking, is a new method of viewing slices dedicated to surface tracking, i.e. fault-picking, from 3D large seismic data sets. The proposed method establishes a new paradigm of interaction breaking with the conventional 2D slices method usually used by geoscientists. Based on the 2D+time visualization method, dynamic picking facilitates localizing of faults by taking advantage of the intrinsic ability of the human visual system to detect dynamic changes in textured data. The second, projective slice, is a focus+context visualization technique that offers the advantage of facilitating the anticipation of upcoming slices over the sloping 3D surface. From the reported experimental results, dynamic picking leads to a good compromise between fitting precision and completeness of picking while the projective slice significantly reduces the amount of workload for an equivalent level of precision. r 2009 Elsevier Ltd. All rights reserved. Keywords: 3D interaction; Volumetric data; Structural interpretation; Manual segmentation; Dynamic picking; Focus+context; Projective slice
1. Introduction Today, surveys based on the interpretation of 3D data are essential for petroleum exploration, modern medicine, archaeology, mechanical engineering, etc. In petroleum and gas exploration, how and where to locate the drilling well is strongly dependent on the relevance of the 3D geological model of the subsurface established by the geologist. To plan drilling, geoscientists design a geological model based on the extent and the distances between inner 3D geo-objects such as geological layers, or other important structures related to tectonic Earth activities. Thus, the potential of the 3D object interaction can be fully exploited to support the decision to locate oil or gas reservoirs. 3D object interaction is a large research area for which numerous works exist concerning issues such as occlusion management and object selection. A taxonomy of 3D Corresponding author. Tel.: +33 6 62 66 61 33; fax: +33 562 173 839.
E-mail addresses: [email protected] (P. Salom), [email protected] (Y. Berthoumieu). 1071-5819/$ - see front matter r 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhcs.2009.01.002
occlusion management techniques is given by Elmqvist and Tsigas (2007) to solve depth perception issues for the 3D visualization context. Some approaches such as importance-driven volume rendering can be used to maximize the information embedded in the final image by using an automatic hierarchical 3D object management in terms of importance and sparseness (Viola et al., 2004). All these techniques are widely used in medical applications such as surgical planning (Kru¨ger et al., 2005). However, in the geological context, a key issue is to precisely construct 3D geo-objects leading to the final geological model. Most often, the segmentation of geological volumetric data, called seismic data, arises as a prerequisite to 3D geo-object interaction. In the computer graphics domain, several volume-rendering techniques are used to directly segment particular objects from 3D data. Most common approaches are based on iso-surface extraction, such as the marching cubes algorithm (Lorensen and Cline, 1987), or pixel-oriented strategies, such as the ray casting algorithm (Drebin et al., 1988). Unfortunately, these approaches rely heavily on the existence of a one-to-one relationship between an object
ARTICLE IN PRESS 552
P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
and data intensity. In the seismic imaging framework, the nature of the data that are heavily textured, noisy and characterised by high frequencies prevents using such techniques. Inner objects are not directly available and specific techniques to segment them from the volumetric data have to be developed. In this way, supervised approaches based on exploring volumetric data to manually segment objects are largely requested by experts. Moreover, semi-supervised approaches can be considered. Sketch-based methods address this issue (Owada et al., 2005, 2008). This paper focuses on the issue of volumetric data interaction concerning manual segmentation. Manual segmentation refers to the perceptuo-motor process whereby a human expert localizes, segments and labels each structure directly on the data set. In the seismic context, this task, called picking, is carried out by selecting points of interest with a pointing device on several slices of a seismic block. Most often, the picking task is an iterative process repeating point selection over slices until the finest description possible of the structure is obtained. Geoscientists currently use a static picking technique consisting of a discrete ‘‘slice-by-slice’’ approach. While manual segmentation seems to be accurate, especially for low signal-tonoise ratios, static picking is extremely demanding in terms of time and workload. Thus we propose modifying the manual segmentation paradigm in order to derive a more efficient interactive process. Our contribution aims to provide a new interaction method called dynamic picking, which constitutes an alternative to conventional strategies based on 2D visualization or pure volume fly-though solutions. Dynamic picking improves shape understanding using a 3D view with animations while reducing the segmentation time and keeping the ease of use of a 2D selection. Moreover, in order to provide more global information on the targeted geological structure while maintaining the integrity of the data, we introduce projective slice as a new focus+context visualization technique.
In the following section, the related works and the geological background from which this work was developed are introduced. Conventional static picking used for manual segmentation in a seismic framework is discussed. Next, the new paradigm of dynamic picking and its advantages are described through an experimental comparison with static picking. Finally, the projective slice is presented and an empirical assessment shows its advantages. 2. Volumetric data and related works Seismic images are obtained by a reflection technique that consists of propagating acoustic waves into the ground by shooting off a succession of artificial seisms. The propagation velocity of the waves is conditioned by the intrinsic nature of the geological layers. The interface between layers that present a difference in acoustic impedance reflects the waves, which are recovered and recorded by sensors located at the surface. The data acquired from the various shooting points are then integrated and processed in order to generate a seismic volume (Fig. 1a). Resulting seismic volumetric data are then committed to human interpretation in order to localize and extract geological structures based on the analysis of 2D slices (Fig. 1b). Two types of structures, which are called horizons and faults, are particularly relevant for geologists. Mapping fault networks is essential to determine the migration pathways of hydrocarbons. Faults can also help trap hydrocarbons and span reservoir fragmentation. Horizons (Fig. 2) are limits between two particular geological layers that are formed by different materials. In terms of seismic data, the horizons are characterized by strong continuities. Faults (Fig. 2) are mainly composed of fractures in the horizons that are the consequences of the relative displacement of rocks. In seismic data, the position of a fault is visually marked by the shift of horizon abutments.
Fig. 1. Seismic volumetric data (a) and seismic slices (b).
ARTICLE IN PRESS P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
553
Fig. 3. A 3D structural model. The fault surfaces have been reconstructed from manual segmentation. Fig. 2. Seismic slice with horizons and fault.
In a single seismic image a fault corresponds to a spatial alignment of strong discontinuities. Historically, seismic interpretation was carried out on paper, but since the 1990s, the generalisation of workstations has replaced the colour pencil method. Today, high-speed computers support automatic methods for horizon tracking and lead to drastic reduction of the process time of the interpretation process. However, computer-aided methods for automatic fault tracking are less efficient than for horizons (Dorn, 1998; Guillon and Keskes, 2004; Jeong et al., 2006; Donias et al., 2007). Moreover, automatic methods provide incomplete results, while the human visual system can still perceive faults. This is strongly related to the non-explicit nature of the appearance of faults. A fault can be defined as a subjective contour formed by a set of line terminators corresponding to the horizon abutments integrated in the pre-attentive step of vision processing. The tendency of the human brain to close incomplete structures leads to the enhanced perception of faults. This visual ability implies that manual segmentation can be considered as an attractive alternative for detecting faults. A realistic implementation consists in developing a visual interface coupled with an interactive device for picking points dedicated to reconstructing faults. In practice, picking is carried out using a conventional technique called static picking (SP). This yields a task of identifying and grabbing a limited set of points, from the individual observation of the current seismic 2D slice. For each slice, the set of data points gives a 2D polyline modelling the intersection of the fault with the plane defined by the current slice. Finally, the whole set of polylines is used within a surface reconstruction algorithm (Boissonnat and Cazals, 2002) to obtain the final 3D structural geo-object given by the polygonal fault approximation (Fig. 3) providing a geo-object. However, various problems can be identified concerning the SP method. First, this technique is carried out on a 2D
planar view. In practical cases, several thousand slices constitute a typical data set. For each slice, approximately ten points need to be selected. The segmentation task appears too complex and a sub-sampled data set is preferred to decrease the number of slices. Typically, one slice out of twenty is analyzed. Thus, the fault is never seen in full resolution. Second, due to the randomness of the 3D fault orientation in the block, the supervised analysis of 2D slices requires visual integration from any orientation. This corresponds to a mental process called mental registration (Tory, 2003), which entails a particularly high cognitive load. Finally, the discrete selection of points imposes several hundred mouse clicks for each fault, which is very tiresome in terms of motor activity. Many of these problems could be solved by adopting a 3D interface. However, new skills should be required to control specific actions in space using interactive tools that may not always correspond to current work practices. To design a tractable interface for exploring volumetric data, several works propose preserving the integrity of the data while magnifying local details. These visualization techniques are generally based on detail-in-context (focus+context) solutions to render the data in 3D shapes instead of traditional 2D slices. This concept was proposed previously with the MagicSphere (Cignoni et al., 1994), a 3D spherical widget that displays the information included in a volume of interest. This widget can be manipulated by the user’s hand via an input device with six degrees of freedom. Associated with a multiresolution filter, this tool can magnify local details while preserving context. The disadvantage of the occluding part of the data is the risk of also removing part of the contextual information. Note that some works proposed combining the 3D objects, generally represented by polygonal approximations, and volumetric data. In their pioneering work Viega et al. (1996) extended the metaphor of see-through interface embodied in Magic Lenses to 3D environments. More recent contributions as in McGuffin et al. (2003) developed ‘‘exploded views’’. McGuffin explored several solutions to
ARTICLE IN PRESS 554
P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
eliminate occlusion for viewing simultaneously volumetric data and inner objects. Using 3D widgets, the user selects different slices to visualize an inner region while the surrounding slices rotate or translate to another location by means of an animation. Ropinski et al. (2006) proposed several metaphors to facilitate the visual exploration of a seismic volume. One of their proposals is a 3D convexshaped lens centered on the observer’s viewpoint to make the interior of the volume visible. In addition to this technique the authors propose displaying the region of interest with different levels of detail to improve the rendering performance. For our study, the concept of ‘‘focus+context’’ appears as a key point but the technique used to magnify region or inner object must be adapted with the picking task. 3. The dynamic picking paradigm As mentioned in the previous part, we are interested in segmenting volumetric data to extract geo-objects, i.e. faults. More specifically, the proposed study aims to develop a method to estimate polygonal approximation of faults based on manual segmentation. Thus, the paper addresses two types of issues that are strongly interdependent in the framework of manual segmentation. The first issue is related to the visualization of volumetric, complex and large data. Can we apply a method to directly visualize embedded structures in these large data sets? How to present the data in a particular manner that limits the cognitive resources required by the expert during visual exploration? The second issue concerns the task’s user for segmenting 3D structures from volumetric data. Even though selecting particular pixels in a 2D environment is a trivial task, the same task in a 3D context is much more complex for users. The reason is mainly due to the limitation of the standard desktop-based paradigm for a 3D interacting task (two-degrees-of-freedom input devices and non-stereoscopic display). Could we carry out the segmentation task in the 3D domain and guarantee an increase in user satisfaction even on standard workstations?
In this paper, the proposed approach is Dynamic picking (DP), which takes into account the limitations of the conventional approach, i.e. static picking (SP). The DP method proposes simultaneously visualizing and animating a user-specified-path-based 2D slice of the volume displayed in a 3D view. This kind of method facilitates the detection and mental registration of 3D structures, without limiting visualization to canonical slices. Moreover, the rendering technique retained to display arbitrary slices preserves the integrity of the data while facilitating the selection of points on the fly with standard pointing devices, i.e mouse, stylus plus tablet. 3.1. Dynamic picking as a procedure The DP method consists in first planning the exploration of the volume with a user-specified path in order to track a given fault. The ability to specify a free path (step 1 in Fig. 4) allows the geoscientist to adapt an automatic navigation according to the main direction of the fault of interest. As shown in Fig. 4, the path is defined in a plane near-orthogonal to the direction of navigation. After this planning step, the automatic animation of perpendicular
Fig. 5. Steps of the dynamic picking of a fault.
Fig. 4. Principle of dynamic picking. (Step 1) The path is defined. (Step 2) the animation of oblique slices is carried out following the path.
ARTICLE IN PRESS P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
oblique slice according to this predefined path can be turned on in order to capture fault-points (step 2 in Fig. 4). Fig. 5 gives the guidelines for the dynamic picking task. Each step is developed in the following sections. The first step corresponds to the user-specified-path definition, which allows the user to define the path for the automatic animation in the volume. The second one proposes the slice animation within a 3D box along the path. The user can select a simple 2D plane or a more complex slice, called the projective slice, to interact with the data. During this step, the picking is recorded, spanning the point cloud dedicated to a given fault. The last step starts the reconstruction algorithm for a selected point cloud. 3.2. User-specified-path definition In the DP method, the camera motion mode enables the route-planning metaphor proposed by Igarashi et al. (1998) to be incorporated in the virtual reality framework. Route planning is the most unobtrusive method to travel in a 3D environment. It consists first in selecting a path trajectory and subsequently providing an automatic displacement of the viewpoint along this trajectory. The user specifies the path by laying down several landmarks by hand directly on the data. Polylines are then used to connect each landmark and generate the final trajectory. In our application, when the interpreter selects the route-planning mode, the viewpoint is automatically moved to be perpendicular to the top slice view, called TimeSlice (Fig. 4). In the seismic framework, the TimeSlice view is considered to be the most contextual way to interpret the fault network in a small vertical area as most of the fault surfaces intersect a TimeSlice at an angle between 451 and 901. When the required trajectory has been obtained, the viewpoint of the camera is replaced along the path and the animation is ready to start. Slices perpendicular to the path are then animated at a specific frame rate. The interpreter can point to the fault located at the center of the view (Fig. 4). 3.3. Animation within a 3D box Automatically changing the displayed slices can be a means to decrease the length of the segmentation process while parallelizing and facilitating the slicing and selection tasks. The key idea is to confront the expert with animated data that improve fault detection. Humans are naturally very sensitive to motion, and displaying a sequence of pictures with a sufficient frame rate allows the visual system to create the illusion of continuous motion (Ramachandran and Anstis, 1986). To establish the apparent motion, the brain is able to rapidly process attributes in input and combine them into a global motion pattern. This global pattern helps to better perceive the contours of the regions and allows the rapid scanning of an entire phenomenon. Moreover, motion is well known to attract user focus on areas of interest and to highlight (‘‘pop-out’’) (Treisman and Gelade, 1980) several objects in
555
a very efficient bottom-up perception process. We claim that animated seismic data improve fault detection and decrease the expert’s workload in the picking task. Each slice of data perpendicular to a user-specified path is visualized ‘‘in situ’’ in a 3D view with a wireframe box representing the edges of the corresponding volume. This principle, similar to a clipping plane, facilitates the spatial perception of 3D structures. Thus the user can easily locate a 2D slice in the volume. No mental rotation is required, unlike a strict 2D display where the cognitive load is higher so as to register the shape of a structure in order to determine which voxels belong to a fault. Tory (2003) proved that the clipping plane is the best mixing 2D/3D visualization technique for mental registration. Visualizing the slices in a 3D view is not very easy to carry out with a static camera. During animation, various slices go back and forth from the user viewpoint, leading to a change in their size and perspective. In order to relieve the user from manipulating the scene camera for each slice displayed, the position and the orientation of the camera are directly linked to the current slice. The camera line of sight is computed so as to be congruent with the normal of the slice visualized. To maintain a constant distance between viewpoint and slice, the position of the camera is dynamically adapted, thus providing a constraint camera motion. Commands to freeze, accelerate, decelerate, invert the animation and zoom are also available on the keyboard. 3.4. Points cloud selection Animation of the perpendicular oblique slices is accomplished according to the path. The camera is moved to be at the same distance and orientation from each frame. The point cloud describing the fault is generated progressively on the fly. Conventionally, the selection of points in SP consists in acquiring several points on static slices. This acquisition task involves the same discrete movement as a standard target acquisition task. In order to increase the bandwidth between the user and the data and to take advantage of the animation, it is suggested that continuous selection could be used in place of discrete acquisition. During animation, the interpreter moves the cursor along the fault while the system automatically acquires a maximum of points until the device button is released. This trajectory movement is isomorphic to the gesture of pencil drawing that geologists used on paper until recently. This new paradigm implies continuous control as the interpreter provides a constant aimed movement throughout the picking process. First Accot and Zhai (1999) categorized and modelized this kind of trajectory task by an extension of the well-known Fitts law (Fitts, 1954). The geometry of the point cloud that comes from continuous picking depends on two factors: the tradeoff between time and precision adopted by the interpreter and the animation frame rate.
ARTICLE IN PRESS 556
P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
3.5. Projective slice As mentioned in the introduction, visualization techniques such as detail-in-context approaches (focus+context) could be used to design tractable interfaces for exploring volumetric data. For instance, Ropinski et al. (2006) used particular geometries for surrounding contextual seismic data. Thus, in the framework of picking, we propose to use this technique to alleviate the main limitation of the DP approach, which appears to have come from the difficulty for an interpreter to anticipate the spatial displacement of the structure from slice to slice. In order to solve this problem, a new multi-context visualization technique, the projective slice, is proposed. The idea of displaying different contexts in one view is well known in the field of information visualization. The fisheye (Furnas, 1986) is a deformation of the view related to a nonlinear function that enlarges a limited surrounding area of a view. This method highlights details inside the area of interest while preserving the surrounding context due to a deformation with a continuous transition. Gutwin and Skopik (2003) showed that this kind of tool, when indexed on the position of the cursor, is particularly effective within the framework of trajectory selection tasks. These viewing strategies have been applied to the 3D context to minimize the cognitive load during understanding of discrete information (Carpendale et al., 1997). But the main disadvantage of using deformation for continuous information is to disturb the spatial relationship of the objects and increase bias in interpretation. Another alternative is proposed with the perspective wall (Mackinlay et al., 1991) to implement deformation, which displays 2D information inside a 3D perspective view. The planar central zone contains information to be analyzed whereas two areas in perspective present the global context. This slice implementation is one way of displaying a large quantity of information in only one view. For DP, we propose to incorporate a detail-in-context tool that combines parts from the present context and parts from the future context data, which is farther inside the volume of an opaque data set. The projective slice allows
the current data to be preserved while including information on the future evolution of the pointed structures. The present context is a portion of the visualized slice, which is automatically adjusted on the position of the cursor as shown in Fig. 6. On both sides, two slanted slices with a variable given slope display a ‘‘progressive’’ future of the volume from the current slice. A perspective effect is added to the slanted slices to provide an indication of the angle dimension between present and future. As preview information is essential for activities in a dynamic environment, we claim that displaying information about the future helps to limit errors. A first description of the theoretical background of this approach can be found in one of our previous works (Salom et al., 2005). 4. Experiments To evaluate the proposed picking implementation, two experiments dedicated to DP evaluation comparatively to static picking (SP) were carried out. To fully appreciate the strengths of the DP approach, a first experiment consisting in segmenting a synthetic and randomised fault surface embedded in a textured volume was conducted. Processing the point clouds spanned by the participants picking, precision and completeness were evaluated. The second experiment aimed to evaluate the interest of the projective slice in terms of time to complete the picking task. The participant followed a synthetic surface with three visualization techniques: Static Picking and Dynamic Picking with and without Projective Slice. 4.1. Picking of synthetic fault In this first experiment, our hypothesis was that the DP compared to the SP could reduce the duration of the segmentation while keeping a limited rate of errors. 4.1.1. Design The experiment consisted in picking a synthetic fault, created by shifting one part of a seismic block along a
Fig. 6. Principle of the projective slice.
ARTICLE IN PRESS P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
surface of equation: f ðxÞ ¼ y=a cos ðy=bÞ þ GðzÞ.
(1)
The previous function was additively separable in y and z, so the surface can become deformed from slice to slice. The z values were defined by means of a succession of random numbers convoluted with a Gaussian function noted G(z). We made this choice to prevent the users from memorizing the locations of the surface intersected by several slices. The synthetic surface used for the evaluation was determined in collaboration with geologists to respect the ground truth related to our study. During animation, the fault moved in a random horizontal smooth motion with a maximum magnitude of 47 pixels and an acceleration of 0.83 pixels per second related to the values of the parameters (a ¼ 40) and (b ¼ 60). The selection of these parameters defined the shape of the fault to provide a plausible geometry from the geological point of view. For each test, the first slice starts by displaying five points in order to indicate the targeted fault to the subjects. The step retained between each section displayed for SP is 30. The frame rate of DP is 8 frames per second. The instruction given to the subject was to perform an accurate selection of the faults in 2 min. 4.1.2. Participants Twelve subjects (9 men and 3 women) of an average age of 31 took part in this experiment. They were all beginners in terms of manual segmentation and were not geologists. In order to avoid laterality problems, all the subjects were right-handed. 4.1.3. Apparatus The experimental environment was composed of two Mitshubishi Diamond 2070 screens (22 in with a resolution of 1280 1024 pixels). The computer was a Pentium IV 3 GHz with 2 GB of RAM and a graphic board GeForce 6800 WP with 256 MB of memory. The pointing device was a LOGITECH optical mouse used with first control order function (speed).
557
4.1.4. Variables The independent variable evaluated during this experiment was the interaction technique made up of the two modalities: SP and DP. The order of the modalities was randomly obtained by a Latin square. Each modality was used three times by the subjects. The first test was simply to familiarize the subjects with the technique. The two others tests were recorded to measure (position x, y, z of the acquired point). The techniques were compared using metrics that reflect the quality of picking. From the interpreter’s point of view, a point cloud is valid if it combines both precision and completeness. To be ‘‘precise’’, the group of dots must be located at the shortest distance possible from the fault surface. The precision was estimated by computing the Euclidean distance between each selected point compared to its orthogonal projection on the fault. The attribute average of these distances was used to obtain the precision metric. To be ‘‘complete’’, the picking must provide points regularly distributed near the surface. Completeness was quantified by the distribution of the points on the fault. All the points were initially projected vertically on the plane (y, z) (Fig. 7). The image was then binarised and a fast marching algorithm was applied (Sethian, 1996). The image obtained with the fast marching method is a distance map where each pixel has a value that corresponds to its distance to the nearest pointing point. The average distance was used to characterise completeness. A low completeness value indicates a homogeneous and dense distribution of the picking points. A T-test was used to compare performance associated with each modality. 4.1.5. Results For precision, the results show an average distance of 2.31 pixels (s.d. 0.31) with SP and 3.07 pixels (s.d. 0.28) with DP (Fig. 8). Subjects were substantially more precise with SP (T(11) ¼ 4.076; po0.05). It was assumed that the results obtained with the DP are linked to the lack of predictability of the environment. In fact, users could not anticipate the variation of the surface intersected by the slices, which lead to local correction of the trajectory adopted during the picking. We argue that this issue is
Fig. 7. Completeness metric obtained with the distance map. (a) Picked points projected onto the virtual surface, (b) distance image and (c) histogram of distance values according to the image (b).
ARTICLE IN PRESS 558
P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
Sixteen subjects were asked to point out the intersection of 3D surfaces with 2D slices using three types of visualization techniques:
Fig. 8. Precision, completeness for static picking (SP) and dynamic picking (DP) in pixels.
compensated by the positive impact of the animation to discriminate the surface embedded in the data. For completeness, the results are also significant (T(11) ¼ 21.565; po0.05). The SP obtains an average value of 15.3 pixels (s.d. 0.99). This technique makes it possible to obtain a much less homogeneous point distribution than DP where the average was 6.6 pixels (s.d. 0.91) (Fig. 8). As predicted, the low number of points that the SP allows substantially reduced bandwidth. The continuous selection in dynamic techniques increases this bandwidth and authorizes a more homogeneous distribution along the fault surface compared to the SP approach. The results obtained are coherent with our hypothesis and show that DP is a strong alternative to the traditional technique. In our experiment, the point cloud has a more complete distribution taking advantage of the animation. Even if the error rate is higher compared to SP, the difference (less than 1 pixel) is acceptable in view of the operational context. A qualitative evaluation performed with six end-users confirmed these results. The total duration of a picking in an operational context is reduced by 5 with DP compared to SP, the end-users estimate being more precise with SP. In order to limit the increase of the error rate, we investigated a new focus+context technique visualization for DP: projective slices.
First, a reference technique to visualize the target using a static slice. Second, a dynamic picking solution based on several 2D slices animated at a constant rate. The target to segment has a variable location between each slice. The third technique, with the same condition as the previous solution but using a projective slice in place of conventional 2D slice.
4.2.1. Design The synthetic surface that a subject must analyse was shown as explicit 2D curves drawn in on a black background, representing the left and right borders of the thick surface. A first order stimulus (black and white) was chosen to evaluate only the perceptuo-motor influence of our tool. The surface had a thickness of 20 pixels and its equation was the same as (1) with parameter a ¼ 10 and b ¼ 30. The animation was performed at 10 slices per second according to the z dimension, except for the static condition. The projective slice was composed of a present context and a future context with an angle of a ¼ 51 located at H ¼ 10 pixels from the top of the cursor position. 4.2.2. Procedure The instruction given to the subjects was to keep their cursor along the visualized intersection of the surface and the slices while moving it from the bottom to the top without going down. For each new test the subjects replaced their cursor in a square located at the bottom of the curve painted in red. The test started as soon as the cursor crossed a horizontal starting line located 50 pixels up from one of the squares. At that moment the border of the surface became green. If the cursor moves too far from the surface (more than 10 pixels) the borders become red to provide feedback on the precision. The test finished once the horizontal finishing line at the top of the screen was crossed. Training tests were repeated until the subjects obtained an error rate below 5% to have a tradeoff between time and error that corresponds to the ground truth.
4.2. Experimental validation of the projective slice A first validation of the projective slice was carried out in a previous paper (Salom et al., 2005) in order to measure the impact of our technique during DP. This preliminary study showed that the project slice significantly contributes to error reduction. However, we obtained a number of errors due to a tradeoff between time and precision carried by the subjects that a geologist would naturally adopt in operational conditions. The protocol was changed to force the subjects to satisfy a strict constraint on the error rate to evaluate our technique only on the movement time metric.
4.2.3. Participants The participants were all volunteers and were not involved in activities related to steering tasks. We requested 16 subjects (10 male and 6 female) of an average age of 27.2 years. To avoid experimental variations generated by laterality, only right-handed subjects were selected. Same apparatus as the first experiment was used. 4.2.4. Variables The independent variable for the study of the visualization technique was composed of three modalities: static
ARTICLE IN PRESS P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
slice, dynamic slice and projective slice. Each subject carried out five tests for each picking case condition of treatment after the training period. The trial order of the modalities was selected randomly in Latin square. The dependent variables were the time of movement (ms) and the error rate (percentage), measured as the number of time that the cursor was more than 10 pixels away from the surface. 4.2.5. Results An ANOVA was applied to compare the mean averages of the error rate but provided no significant results (F(14,2) ¼ 0.822; p ¼ 0.452). However, we can observe that the error rate was very low with all three techniques. These results show that the subjects respected the protocol and adopted a conservative behaviour (less than 5% of error rate). The subjects were trained to increase precision instead of reducing their movement time. They made 1.2% errors with the static slice, 1.9% with the projective slice and finally 2% with the dynamic slice. We obtain significant results for the movement time with an ANOVA (F(14,2) ¼ 11.75; po0.05). T-Test for paired groups was applied to compare the mean averages of the movement time between each technique. The results are significant except for the comparison between the static slice and the projective slice (T(15) ¼ 1.944; p ¼ 0.071). The subjects obtained higher results with the static slice 7.467 s (s.d. 2.5) compared to the dynamic slice 9.420 s (s.d. 2.8) (T(15) ¼ 4.525; po0.001), which is consistent with our hypothesis. The projective slice permits a decrease in movements 8.171 s (s.d. 2.05) compared to the dynamic slice (T(15) ¼ 2.739; po0.05)) (see Fig. 9). The issues related to DP still remain due to the difficulties in anticipating the variation of the intersected surface in closed loop conditions compared to results obtained with the static technique. Subjects succeeded in reducing these disadvantages based on data previsualization. Using the projective slice, the subjects were both more precise and rapid compared to the dynamic slice. The
Fig. 9. Movement time (ms) for standard and projective slice (ms).
559
subjective sensations were noted, showing that all subjects preferred to visualize the surface with the projective slice rather than the dynamic slice. They felt more comfortable and could adapt their pointing to the variation of the surface to segment. From the operational point of view, it would seem that the projective slice could accelerate the time to point while preserving the criterion related to precision. It could moreover be used to eliminate numerous local ambiguities by providing 3D information on the global shape of a fault and of the entire fault network. 5. Discussion We performed a qualitative evaluation with six end-users to complete this experiment and it confirmed these results. We implemented our technique in the software used by geologists and asked them to segment the same fault using SP and DP. We measured the time spent by each expert to obtain a result that could be estimated as satisfying. Using a questionnaire, we gathered user judgments concerning the generated point cloud in terms of completeness and precision. The results showed that the total duration of picking in an operational context was reduced by 5 with DP compared to SP. The end-users also estimated the result to be more precise with SP and more complete with DP. All the users validated the point that animation improves and facilitates the localization of a fault. They also considered that our solution can facilitate cooperation and iteration between experts as it enables them to obtain a quick draft of a geological model. Our technique for surface recovery allows users to detect amplitude, texture or subjective contours. The dynamic picking is dedicated to tracking thin surfaces in any applicative context. However, for blob segmentation, our approach is not adapted and must be redesigned. Tracking closed edges with actual implementation leads to a sparse point cloud. In perspective, several applications need to extract a specific region of interest in a volumetric data without segmentating the whole volume. In medical applications such as arthroscopy, colonoscopy or aortic aneurysms for instance using Magnetic Resonance Images (MRI) or Computerized Tomography (CT) data, it is of interest to obtain spatial information on specific structures without occlusion or other problems linked to the 3D interaction. Some recent works, such as sketch-based methods (Owada et al., 2005, 2008), proposed, first, to realize a 2D rough sketch of the foreground and background regions and, second, to propagate the a priori to segment other following slices. The volume catcher system (Owada et al., 2008) proposes some implementations of special 2D interface tools for selecting a region of interest. The first module consists in a 2D image-processing module taking into account the user’s 2D gestures such as click, axis or free lines. The second one is the catcher module, which realizes the 3D subregion segmentation constrained by the a priori provided by the 2D area selected in the previous
ARTICLE IN PRESS 560
P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
step. The main drawback of the method is that the geometry of the 3D subregion deduced to the 2D module is constrained. Thus, we think that dynamic picking can be introduced to provide the user’s 3D gesture. 6. Conclusions In this paper, we have considered the specific problem of the manual segmentation or picking of underlying surfaces embedded in volumetric data. We have proposed a 3D path-based approach using animation of orthogonal slices to visualise and interact: the dynamic picking paradigm. There are two main advantages of the proposed method compared to the conventional 2D static picking technique: volumetric perception of the data and more rapid treatment of the volume. The animation of the data along a constrained path provides a method whereby an operator may continually analyse input textured data, allowing him to increase his perception and his conceptualisation of the tracked surfaces. Moreover, a multi-contextual technique of visualization, called projective slice, has been considered reducing the problems related to the dynamic aspect of the proposed method. Thanks to continuous navigation and projective slice, our conclusions, validated by experimental results, indicate the numerical signature of the surface produced by the operator using the dynamic pointing interface is characterised by an increased completeness for a substantial decrease of precision. In our opinion, pragmatic user-centered approach respects the expert’s work method while introducing new concepts that make easy a time-consuming and tiresome activity. Acknowledgement This work is supported by the Total Company. The content of this paper is the subject of the Total company patent (Keskes et al., 2006). The authors would like to thank Total company for the supply of the data. References Accot, J., Zhai, S., 1999. Performance evaluation of input devices in trajectory-based tasks: an application of the steering law. In: Proceedings of the Conference on Human Factors in Computing Systems, pp. 466–472. Boissonnat, J.D., Cazals, F., 2002. Smooth surface reconstruction via natural neighbor interpolation of distance functions. Computational Geometry: Theory and Applications 22 (1), 185–203. Carpendale, M.S.T., Cowperthwaite, D.J., Fracchia, F.D., 1997. Extending distortion viewing from 2D to 3D. Computer Graphics and Applications, 42–51. Cignoni, P., Montani, C., Scopigno, R., 1994. MagicSphere: an insight tool for 3D data visualization. Computer graphics Forum 13 (3), 317–328. Donias, M., David, C., Berthoumieu, Y., Lavialle, O., Guillon, S., Keskes, N., 2007. New fault attribute based on robust directional scheme. Geophysics 72 (4), 39–46.
Dorn, G., 1998. A modern 3-D seismic interpretation. The Leading Edge 17, 1262–1273. Drebin, R.A., Carpenter, L., Hanrahan, P., 1988. Volume rendering. Computer Graphics 22 (4), 65–74. Elmqvist, N., Tsigas, P., 2007. A taxonomy of 3D occlusion management techniques. In: Proceedings of the IEEE Conference on Virtual Reality, pp. 51–58. Fitts, P.M., 1954. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology 4, 381–391. Furnas, G.W., 1986. Generalized fisheye views. In: Proceedings of the Conference on Human Factors in Computing Systems, pp. 16–23. Guillon, S., Keskes, N., 2004. Sismage and the 3D visualization at total. In: AAPG International Conference. Gutwin, C., Skopik, A., 2003. Fisheyes are good for large steering tasks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 2003, pp. 201–208. Igarashi, T., Kadobayashi, R., Mase, K., Tanaka, H., 1998. Path drawing for 3D walkthrough. In: Proceedings of the 11th Annual ACM Symposium on User Interface Software and Technology, pp. 173–174. Jeong, W.K., Whitaker, R., Dobin, M., 2006. Interactive 3D seismic fault detection on the graphics hardware. In: Proceedings International Workshop on Volume Graphics, pp. 111–118. Keskes, N., Donias, M., Berthoumieu, Y., Guillon, S., 2006. Method and program for fracture plane reconstruction. United States Patent 20060190214. Kru¨ger, C., Tietjen, J., Hintze, B., Preim, I., Hertel, G., Strauss. 2005. Interactive Visualization for Neck-Dissection Planning. Eurographics 2005. Lorensen, W.E., Cline, H.E., 1987. Marching cubes: a high resolution 3D surface construction algorithm. Computer Graphics 21 (4), 163–169. Mackinlay, J., Robertson, G., Card, S., 1991. The perspective wall: detail and context smoothly integrated. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 56–63. McGuffin, M.J., Tancau, L., Balakrishnan, R., 2003. Using deformations for browsing volumetric data. In: Proceedings of IEEE Visualization (VIS), pp. 401–408. Owada, S., Nielsen, F., Igarashi, T., 2005. Volume catcher. In: Proceedings of the ACM Symposium on Interactive 3D Graphics and Games, pp. 111–116. Owada, S., Nielsen, F., Igarashi, T., Haraguchi, R., Nakazawa, K., 2008. Projection plane processing for sketch-based volume segmentation. IEEE International Symposium on Biomedical Imaging, 117–120. Ramachandran, V.S., Anstis, S.M., 1986. The perception of apparent motion. Scientific American 254, 102–109. Ropinski, T., Steinicke, F., Hinrichs, K., 2006. Visual exploration of seismic volume datasets. In: Proceedings of the 14th International Conference in Central Europe on Computer Graphics and Computer Vision, pp. 73–80. Salom, P., Becerra, J., Donias, M., Megret, R., 2005. Projective slice for a dynamic steering task. In: Proceedings of the ACM Symposium on Virtual Reality Software and Technology, pp. 138–141. Sethian, J.A., 1996. Level-set methods. Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision and Material Science. Cambridge University Press. Tory, M., 2003. Mental registration of 2D and 3D visualizations (an empirical study). In: Proceedings of the 14th IEEE Visualization, pp. 371–378. Treisman, A.M., Gelade, G., 1980. A feature-integration theory of attention. Cognitive Psychology 12, 97–136. Viega, J., Conway, M. J., Williams, G., Pausch, R., 1996. 3D magic lenses. In: Proceedings of the 9th Annual ACM Symposium on User Interface Software and Technology, pp. 51–58. Viola, I., Kanitsar, A., Groller, M.E., 2004. Importance-driven volume rendering. Proceedings of IEEE Visualization, 39–145.
Int. J. Human-Computer Studies 67 (2009) 551–560 www.elsevier.com/locate/ijhcs
Dynamic picking system for 3D seismic data: Design and evaluation Pierre Salom, Remi Megret, Marc Donias, Yannick Berthoumieu LAPS, IMS, CNRS UMR 5218, Universite´ Bordeaux 1, 351 cours de la Libe´ration, 33405 Talence, France Received 16 August 2007; received in revised form 23 December 2008; accepted 23 January 2009 Communicated by J. Stasko Available online 11 March 2009
Abstract In the framework of data interpretation for petroleum exploration, this paper contributes two contributions for visual exploration aiming to manually segment surfaces embedded in volumetric data. Resulting from a user-centered design approach, the first contribution, dynamic picking, is a new method of viewing slices dedicated to surface tracking, i.e. fault-picking, from 3D large seismic data sets. The proposed method establishes a new paradigm of interaction breaking with the conventional 2D slices method usually used by geoscientists. Based on the 2D+time visualization method, dynamic picking facilitates localizing of faults by taking advantage of the intrinsic ability of the human visual system to detect dynamic changes in textured data. The second, projective slice, is a focus+context visualization technique that offers the advantage of facilitating the anticipation of upcoming slices over the sloping 3D surface. From the reported experimental results, dynamic picking leads to a good compromise between fitting precision and completeness of picking while the projective slice significantly reduces the amount of workload for an equivalent level of precision. r 2009 Elsevier Ltd. All rights reserved. Keywords: 3D interaction; Volumetric data; Structural interpretation; Manual segmentation; Dynamic picking; Focus+context; Projective slice
1. Introduction Today, surveys based on the interpretation of 3D data are essential for petroleum exploration, modern medicine, archaeology, mechanical engineering, etc. In petroleum and gas exploration, how and where to locate the drilling well is strongly dependent on the relevance of the 3D geological model of the subsurface established by the geologist. To plan drilling, geoscientists design a geological model based on the extent and the distances between inner 3D geo-objects such as geological layers, or other important structures related to tectonic Earth activities. Thus, the potential of the 3D object interaction can be fully exploited to support the decision to locate oil or gas reservoirs. 3D object interaction is a large research area for which numerous works exist concerning issues such as occlusion management and object selection. A taxonomy of 3D Corresponding author. Tel.: +33 6 62 66 61 33; fax: +33 562 173 839.
E-mail addresses: [email protected] (P. Salom), [email protected] (Y. Berthoumieu). 1071-5819/$ - see front matter r 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhcs.2009.01.002
occlusion management techniques is given by Elmqvist and Tsigas (2007) to solve depth perception issues for the 3D visualization context. Some approaches such as importance-driven volume rendering can be used to maximize the information embedded in the final image by using an automatic hierarchical 3D object management in terms of importance and sparseness (Viola et al., 2004). All these techniques are widely used in medical applications such as surgical planning (Kru¨ger et al., 2005). However, in the geological context, a key issue is to precisely construct 3D geo-objects leading to the final geological model. Most often, the segmentation of geological volumetric data, called seismic data, arises as a prerequisite to 3D geo-object interaction. In the computer graphics domain, several volume-rendering techniques are used to directly segment particular objects from 3D data. Most common approaches are based on iso-surface extraction, such as the marching cubes algorithm (Lorensen and Cline, 1987), or pixel-oriented strategies, such as the ray casting algorithm (Drebin et al., 1988). Unfortunately, these approaches rely heavily on the existence of a one-to-one relationship between an object
ARTICLE IN PRESS 552
P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
and data intensity. In the seismic imaging framework, the nature of the data that are heavily textured, noisy and characterised by high frequencies prevents using such techniques. Inner objects are not directly available and specific techniques to segment them from the volumetric data have to be developed. In this way, supervised approaches based on exploring volumetric data to manually segment objects are largely requested by experts. Moreover, semi-supervised approaches can be considered. Sketch-based methods address this issue (Owada et al., 2005, 2008). This paper focuses on the issue of volumetric data interaction concerning manual segmentation. Manual segmentation refers to the perceptuo-motor process whereby a human expert localizes, segments and labels each structure directly on the data set. In the seismic context, this task, called picking, is carried out by selecting points of interest with a pointing device on several slices of a seismic block. Most often, the picking task is an iterative process repeating point selection over slices until the finest description possible of the structure is obtained. Geoscientists currently use a static picking technique consisting of a discrete ‘‘slice-by-slice’’ approach. While manual segmentation seems to be accurate, especially for low signal-tonoise ratios, static picking is extremely demanding in terms of time and workload. Thus we propose modifying the manual segmentation paradigm in order to derive a more efficient interactive process. Our contribution aims to provide a new interaction method called dynamic picking, which constitutes an alternative to conventional strategies based on 2D visualization or pure volume fly-though solutions. Dynamic picking improves shape understanding using a 3D view with animations while reducing the segmentation time and keeping the ease of use of a 2D selection. Moreover, in order to provide more global information on the targeted geological structure while maintaining the integrity of the data, we introduce projective slice as a new focus+context visualization technique.
In the following section, the related works and the geological background from which this work was developed are introduced. Conventional static picking used for manual segmentation in a seismic framework is discussed. Next, the new paradigm of dynamic picking and its advantages are described through an experimental comparison with static picking. Finally, the projective slice is presented and an empirical assessment shows its advantages. 2. Volumetric data and related works Seismic images are obtained by a reflection technique that consists of propagating acoustic waves into the ground by shooting off a succession of artificial seisms. The propagation velocity of the waves is conditioned by the intrinsic nature of the geological layers. The interface between layers that present a difference in acoustic impedance reflects the waves, which are recovered and recorded by sensors located at the surface. The data acquired from the various shooting points are then integrated and processed in order to generate a seismic volume (Fig. 1a). Resulting seismic volumetric data are then committed to human interpretation in order to localize and extract geological structures based on the analysis of 2D slices (Fig. 1b). Two types of structures, which are called horizons and faults, are particularly relevant for geologists. Mapping fault networks is essential to determine the migration pathways of hydrocarbons. Faults can also help trap hydrocarbons and span reservoir fragmentation. Horizons (Fig. 2) are limits between two particular geological layers that are formed by different materials. In terms of seismic data, the horizons are characterized by strong continuities. Faults (Fig. 2) are mainly composed of fractures in the horizons that are the consequences of the relative displacement of rocks. In seismic data, the position of a fault is visually marked by the shift of horizon abutments.
Fig. 1. Seismic volumetric data (a) and seismic slices (b).
ARTICLE IN PRESS P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
553
Fig. 3. A 3D structural model. The fault surfaces have been reconstructed from manual segmentation. Fig. 2. Seismic slice with horizons and fault.
In a single seismic image a fault corresponds to a spatial alignment of strong discontinuities. Historically, seismic interpretation was carried out on paper, but since the 1990s, the generalisation of workstations has replaced the colour pencil method. Today, high-speed computers support automatic methods for horizon tracking and lead to drastic reduction of the process time of the interpretation process. However, computer-aided methods for automatic fault tracking are less efficient than for horizons (Dorn, 1998; Guillon and Keskes, 2004; Jeong et al., 2006; Donias et al., 2007). Moreover, automatic methods provide incomplete results, while the human visual system can still perceive faults. This is strongly related to the non-explicit nature of the appearance of faults. A fault can be defined as a subjective contour formed by a set of line terminators corresponding to the horizon abutments integrated in the pre-attentive step of vision processing. The tendency of the human brain to close incomplete structures leads to the enhanced perception of faults. This visual ability implies that manual segmentation can be considered as an attractive alternative for detecting faults. A realistic implementation consists in developing a visual interface coupled with an interactive device for picking points dedicated to reconstructing faults. In practice, picking is carried out using a conventional technique called static picking (SP). This yields a task of identifying and grabbing a limited set of points, from the individual observation of the current seismic 2D slice. For each slice, the set of data points gives a 2D polyline modelling the intersection of the fault with the plane defined by the current slice. Finally, the whole set of polylines is used within a surface reconstruction algorithm (Boissonnat and Cazals, 2002) to obtain the final 3D structural geo-object given by the polygonal fault approximation (Fig. 3) providing a geo-object. However, various problems can be identified concerning the SP method. First, this technique is carried out on a 2D
planar view. In practical cases, several thousand slices constitute a typical data set. For each slice, approximately ten points need to be selected. The segmentation task appears too complex and a sub-sampled data set is preferred to decrease the number of slices. Typically, one slice out of twenty is analyzed. Thus, the fault is never seen in full resolution. Second, due to the randomness of the 3D fault orientation in the block, the supervised analysis of 2D slices requires visual integration from any orientation. This corresponds to a mental process called mental registration (Tory, 2003), which entails a particularly high cognitive load. Finally, the discrete selection of points imposes several hundred mouse clicks for each fault, which is very tiresome in terms of motor activity. Many of these problems could be solved by adopting a 3D interface. However, new skills should be required to control specific actions in space using interactive tools that may not always correspond to current work practices. To design a tractable interface for exploring volumetric data, several works propose preserving the integrity of the data while magnifying local details. These visualization techniques are generally based on detail-in-context (focus+context) solutions to render the data in 3D shapes instead of traditional 2D slices. This concept was proposed previously with the MagicSphere (Cignoni et al., 1994), a 3D spherical widget that displays the information included in a volume of interest. This widget can be manipulated by the user’s hand via an input device with six degrees of freedom. Associated with a multiresolution filter, this tool can magnify local details while preserving context. The disadvantage of the occluding part of the data is the risk of also removing part of the contextual information. Note that some works proposed combining the 3D objects, generally represented by polygonal approximations, and volumetric data. In their pioneering work Viega et al. (1996) extended the metaphor of see-through interface embodied in Magic Lenses to 3D environments. More recent contributions as in McGuffin et al. (2003) developed ‘‘exploded views’’. McGuffin explored several solutions to
ARTICLE IN PRESS 554
P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
eliminate occlusion for viewing simultaneously volumetric data and inner objects. Using 3D widgets, the user selects different slices to visualize an inner region while the surrounding slices rotate or translate to another location by means of an animation. Ropinski et al. (2006) proposed several metaphors to facilitate the visual exploration of a seismic volume. One of their proposals is a 3D convexshaped lens centered on the observer’s viewpoint to make the interior of the volume visible. In addition to this technique the authors propose displaying the region of interest with different levels of detail to improve the rendering performance. For our study, the concept of ‘‘focus+context’’ appears as a key point but the technique used to magnify region or inner object must be adapted with the picking task. 3. The dynamic picking paradigm As mentioned in the previous part, we are interested in segmenting volumetric data to extract geo-objects, i.e. faults. More specifically, the proposed study aims to develop a method to estimate polygonal approximation of faults based on manual segmentation. Thus, the paper addresses two types of issues that are strongly interdependent in the framework of manual segmentation. The first issue is related to the visualization of volumetric, complex and large data. Can we apply a method to directly visualize embedded structures in these large data sets? How to present the data in a particular manner that limits the cognitive resources required by the expert during visual exploration? The second issue concerns the task’s user for segmenting 3D structures from volumetric data. Even though selecting particular pixels in a 2D environment is a trivial task, the same task in a 3D context is much more complex for users. The reason is mainly due to the limitation of the standard desktop-based paradigm for a 3D interacting task (two-degrees-of-freedom input devices and non-stereoscopic display). Could we carry out the segmentation task in the 3D domain and guarantee an increase in user satisfaction even on standard workstations?
In this paper, the proposed approach is Dynamic picking (DP), which takes into account the limitations of the conventional approach, i.e. static picking (SP). The DP method proposes simultaneously visualizing and animating a user-specified-path-based 2D slice of the volume displayed in a 3D view. This kind of method facilitates the detection and mental registration of 3D structures, without limiting visualization to canonical slices. Moreover, the rendering technique retained to display arbitrary slices preserves the integrity of the data while facilitating the selection of points on the fly with standard pointing devices, i.e mouse, stylus plus tablet. 3.1. Dynamic picking as a procedure The DP method consists in first planning the exploration of the volume with a user-specified path in order to track a given fault. The ability to specify a free path (step 1 in Fig. 4) allows the geoscientist to adapt an automatic navigation according to the main direction of the fault of interest. As shown in Fig. 4, the path is defined in a plane near-orthogonal to the direction of navigation. After this planning step, the automatic animation of perpendicular
Fig. 5. Steps of the dynamic picking of a fault.
Fig. 4. Principle of dynamic picking. (Step 1) The path is defined. (Step 2) the animation of oblique slices is carried out following the path.
ARTICLE IN PRESS P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
oblique slice according to this predefined path can be turned on in order to capture fault-points (step 2 in Fig. 4). Fig. 5 gives the guidelines for the dynamic picking task. Each step is developed in the following sections. The first step corresponds to the user-specified-path definition, which allows the user to define the path for the automatic animation in the volume. The second one proposes the slice animation within a 3D box along the path. The user can select a simple 2D plane or a more complex slice, called the projective slice, to interact with the data. During this step, the picking is recorded, spanning the point cloud dedicated to a given fault. The last step starts the reconstruction algorithm for a selected point cloud. 3.2. User-specified-path definition In the DP method, the camera motion mode enables the route-planning metaphor proposed by Igarashi et al. (1998) to be incorporated in the virtual reality framework. Route planning is the most unobtrusive method to travel in a 3D environment. It consists first in selecting a path trajectory and subsequently providing an automatic displacement of the viewpoint along this trajectory. The user specifies the path by laying down several landmarks by hand directly on the data. Polylines are then used to connect each landmark and generate the final trajectory. In our application, when the interpreter selects the route-planning mode, the viewpoint is automatically moved to be perpendicular to the top slice view, called TimeSlice (Fig. 4). In the seismic framework, the TimeSlice view is considered to be the most contextual way to interpret the fault network in a small vertical area as most of the fault surfaces intersect a TimeSlice at an angle between 451 and 901. When the required trajectory has been obtained, the viewpoint of the camera is replaced along the path and the animation is ready to start. Slices perpendicular to the path are then animated at a specific frame rate. The interpreter can point to the fault located at the center of the view (Fig. 4). 3.3. Animation within a 3D box Automatically changing the displayed slices can be a means to decrease the length of the segmentation process while parallelizing and facilitating the slicing and selection tasks. The key idea is to confront the expert with animated data that improve fault detection. Humans are naturally very sensitive to motion, and displaying a sequence of pictures with a sufficient frame rate allows the visual system to create the illusion of continuous motion (Ramachandran and Anstis, 1986). To establish the apparent motion, the brain is able to rapidly process attributes in input and combine them into a global motion pattern. This global pattern helps to better perceive the contours of the regions and allows the rapid scanning of an entire phenomenon. Moreover, motion is well known to attract user focus on areas of interest and to highlight (‘‘pop-out’’) (Treisman and Gelade, 1980) several objects in
555
a very efficient bottom-up perception process. We claim that animated seismic data improve fault detection and decrease the expert’s workload in the picking task. Each slice of data perpendicular to a user-specified path is visualized ‘‘in situ’’ in a 3D view with a wireframe box representing the edges of the corresponding volume. This principle, similar to a clipping plane, facilitates the spatial perception of 3D structures. Thus the user can easily locate a 2D slice in the volume. No mental rotation is required, unlike a strict 2D display where the cognitive load is higher so as to register the shape of a structure in order to determine which voxels belong to a fault. Tory (2003) proved that the clipping plane is the best mixing 2D/3D visualization technique for mental registration. Visualizing the slices in a 3D view is not very easy to carry out with a static camera. During animation, various slices go back and forth from the user viewpoint, leading to a change in their size and perspective. In order to relieve the user from manipulating the scene camera for each slice displayed, the position and the orientation of the camera are directly linked to the current slice. The camera line of sight is computed so as to be congruent with the normal of the slice visualized. To maintain a constant distance between viewpoint and slice, the position of the camera is dynamically adapted, thus providing a constraint camera motion. Commands to freeze, accelerate, decelerate, invert the animation and zoom are also available on the keyboard. 3.4. Points cloud selection Animation of the perpendicular oblique slices is accomplished according to the path. The camera is moved to be at the same distance and orientation from each frame. The point cloud describing the fault is generated progressively on the fly. Conventionally, the selection of points in SP consists in acquiring several points on static slices. This acquisition task involves the same discrete movement as a standard target acquisition task. In order to increase the bandwidth between the user and the data and to take advantage of the animation, it is suggested that continuous selection could be used in place of discrete acquisition. During animation, the interpreter moves the cursor along the fault while the system automatically acquires a maximum of points until the device button is released. This trajectory movement is isomorphic to the gesture of pencil drawing that geologists used on paper until recently. This new paradigm implies continuous control as the interpreter provides a constant aimed movement throughout the picking process. First Accot and Zhai (1999) categorized and modelized this kind of trajectory task by an extension of the well-known Fitts law (Fitts, 1954). The geometry of the point cloud that comes from continuous picking depends on two factors: the tradeoff between time and precision adopted by the interpreter and the animation frame rate.
ARTICLE IN PRESS 556
P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
3.5. Projective slice As mentioned in the introduction, visualization techniques such as detail-in-context approaches (focus+context) could be used to design tractable interfaces for exploring volumetric data. For instance, Ropinski et al. (2006) used particular geometries for surrounding contextual seismic data. Thus, in the framework of picking, we propose to use this technique to alleviate the main limitation of the DP approach, which appears to have come from the difficulty for an interpreter to anticipate the spatial displacement of the structure from slice to slice. In order to solve this problem, a new multi-context visualization technique, the projective slice, is proposed. The idea of displaying different contexts in one view is well known in the field of information visualization. The fisheye (Furnas, 1986) is a deformation of the view related to a nonlinear function that enlarges a limited surrounding area of a view. This method highlights details inside the area of interest while preserving the surrounding context due to a deformation with a continuous transition. Gutwin and Skopik (2003) showed that this kind of tool, when indexed on the position of the cursor, is particularly effective within the framework of trajectory selection tasks. These viewing strategies have been applied to the 3D context to minimize the cognitive load during understanding of discrete information (Carpendale et al., 1997). But the main disadvantage of using deformation for continuous information is to disturb the spatial relationship of the objects and increase bias in interpretation. Another alternative is proposed with the perspective wall (Mackinlay et al., 1991) to implement deformation, which displays 2D information inside a 3D perspective view. The planar central zone contains information to be analyzed whereas two areas in perspective present the global context. This slice implementation is one way of displaying a large quantity of information in only one view. For DP, we propose to incorporate a detail-in-context tool that combines parts from the present context and parts from the future context data, which is farther inside the volume of an opaque data set. The projective slice allows
the current data to be preserved while including information on the future evolution of the pointed structures. The present context is a portion of the visualized slice, which is automatically adjusted on the position of the cursor as shown in Fig. 6. On both sides, two slanted slices with a variable given slope display a ‘‘progressive’’ future of the volume from the current slice. A perspective effect is added to the slanted slices to provide an indication of the angle dimension between present and future. As preview information is essential for activities in a dynamic environment, we claim that displaying information about the future helps to limit errors. A first description of the theoretical background of this approach can be found in one of our previous works (Salom et al., 2005). 4. Experiments To evaluate the proposed picking implementation, two experiments dedicated to DP evaluation comparatively to static picking (SP) were carried out. To fully appreciate the strengths of the DP approach, a first experiment consisting in segmenting a synthetic and randomised fault surface embedded in a textured volume was conducted. Processing the point clouds spanned by the participants picking, precision and completeness were evaluated. The second experiment aimed to evaluate the interest of the projective slice in terms of time to complete the picking task. The participant followed a synthetic surface with three visualization techniques: Static Picking and Dynamic Picking with and without Projective Slice. 4.1. Picking of synthetic fault In this first experiment, our hypothesis was that the DP compared to the SP could reduce the duration of the segmentation while keeping a limited rate of errors. 4.1.1. Design The experiment consisted in picking a synthetic fault, created by shifting one part of a seismic block along a
Fig. 6. Principle of the projective slice.
ARTICLE IN PRESS P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
surface of equation: f ðxÞ ¼ y=a cos ðy=bÞ þ GðzÞ.
(1)
The previous function was additively separable in y and z, so the surface can become deformed from slice to slice. The z values were defined by means of a succession of random numbers convoluted with a Gaussian function noted G(z). We made this choice to prevent the users from memorizing the locations of the surface intersected by several slices. The synthetic surface used for the evaluation was determined in collaboration with geologists to respect the ground truth related to our study. During animation, the fault moved in a random horizontal smooth motion with a maximum magnitude of 47 pixels and an acceleration of 0.83 pixels per second related to the values of the parameters (a ¼ 40) and (b ¼ 60). The selection of these parameters defined the shape of the fault to provide a plausible geometry from the geological point of view. For each test, the first slice starts by displaying five points in order to indicate the targeted fault to the subjects. The step retained between each section displayed for SP is 30. The frame rate of DP is 8 frames per second. The instruction given to the subject was to perform an accurate selection of the faults in 2 min. 4.1.2. Participants Twelve subjects (9 men and 3 women) of an average age of 31 took part in this experiment. They were all beginners in terms of manual segmentation and were not geologists. In order to avoid laterality problems, all the subjects were right-handed. 4.1.3. Apparatus The experimental environment was composed of two Mitshubishi Diamond 2070 screens (22 in with a resolution of 1280 1024 pixels). The computer was a Pentium IV 3 GHz with 2 GB of RAM and a graphic board GeForce 6800 WP with 256 MB of memory. The pointing device was a LOGITECH optical mouse used with first control order function (speed).
557
4.1.4. Variables The independent variable evaluated during this experiment was the interaction technique made up of the two modalities: SP and DP. The order of the modalities was randomly obtained by a Latin square. Each modality was used three times by the subjects. The first test was simply to familiarize the subjects with the technique. The two others tests were recorded to measure (position x, y, z of the acquired point). The techniques were compared using metrics that reflect the quality of picking. From the interpreter’s point of view, a point cloud is valid if it combines both precision and completeness. To be ‘‘precise’’, the group of dots must be located at the shortest distance possible from the fault surface. The precision was estimated by computing the Euclidean distance between each selected point compared to its orthogonal projection on the fault. The attribute average of these distances was used to obtain the precision metric. To be ‘‘complete’’, the picking must provide points regularly distributed near the surface. Completeness was quantified by the distribution of the points on the fault. All the points were initially projected vertically on the plane (y, z) (Fig. 7). The image was then binarised and a fast marching algorithm was applied (Sethian, 1996). The image obtained with the fast marching method is a distance map where each pixel has a value that corresponds to its distance to the nearest pointing point. The average distance was used to characterise completeness. A low completeness value indicates a homogeneous and dense distribution of the picking points. A T-test was used to compare performance associated with each modality. 4.1.5. Results For precision, the results show an average distance of 2.31 pixels (s.d. 0.31) with SP and 3.07 pixels (s.d. 0.28) with DP (Fig. 8). Subjects were substantially more precise with SP (T(11) ¼ 4.076; po0.05). It was assumed that the results obtained with the DP are linked to the lack of predictability of the environment. In fact, users could not anticipate the variation of the surface intersected by the slices, which lead to local correction of the trajectory adopted during the picking. We argue that this issue is
Fig. 7. Completeness metric obtained with the distance map. (a) Picked points projected onto the virtual surface, (b) distance image and (c) histogram of distance values according to the image (b).
ARTICLE IN PRESS 558
P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
Sixteen subjects were asked to point out the intersection of 3D surfaces with 2D slices using three types of visualization techniques:
Fig. 8. Precision, completeness for static picking (SP) and dynamic picking (DP) in pixels.
compensated by the positive impact of the animation to discriminate the surface embedded in the data. For completeness, the results are also significant (T(11) ¼ 21.565; po0.05). The SP obtains an average value of 15.3 pixels (s.d. 0.99). This technique makes it possible to obtain a much less homogeneous point distribution than DP where the average was 6.6 pixels (s.d. 0.91) (Fig. 8). As predicted, the low number of points that the SP allows substantially reduced bandwidth. The continuous selection in dynamic techniques increases this bandwidth and authorizes a more homogeneous distribution along the fault surface compared to the SP approach. The results obtained are coherent with our hypothesis and show that DP is a strong alternative to the traditional technique. In our experiment, the point cloud has a more complete distribution taking advantage of the animation. Even if the error rate is higher compared to SP, the difference (less than 1 pixel) is acceptable in view of the operational context. A qualitative evaluation performed with six end-users confirmed these results. The total duration of a picking in an operational context is reduced by 5 with DP compared to SP, the end-users estimate being more precise with SP. In order to limit the increase of the error rate, we investigated a new focus+context technique visualization for DP: projective slices.
First, a reference technique to visualize the target using a static slice. Second, a dynamic picking solution based on several 2D slices animated at a constant rate. The target to segment has a variable location between each slice. The third technique, with the same condition as the previous solution but using a projective slice in place of conventional 2D slice.
4.2.1. Design The synthetic surface that a subject must analyse was shown as explicit 2D curves drawn in on a black background, representing the left and right borders of the thick surface. A first order stimulus (black and white) was chosen to evaluate only the perceptuo-motor influence of our tool. The surface had a thickness of 20 pixels and its equation was the same as (1) with parameter a ¼ 10 and b ¼ 30. The animation was performed at 10 slices per second according to the z dimension, except for the static condition. The projective slice was composed of a present context and a future context with an angle of a ¼ 51 located at H ¼ 10 pixels from the top of the cursor position. 4.2.2. Procedure The instruction given to the subjects was to keep their cursor along the visualized intersection of the surface and the slices while moving it from the bottom to the top without going down. For each new test the subjects replaced their cursor in a square located at the bottom of the curve painted in red. The test started as soon as the cursor crossed a horizontal starting line located 50 pixels up from one of the squares. At that moment the border of the surface became green. If the cursor moves too far from the surface (more than 10 pixels) the borders become red to provide feedback on the precision. The test finished once the horizontal finishing line at the top of the screen was crossed. Training tests were repeated until the subjects obtained an error rate below 5% to have a tradeoff between time and error that corresponds to the ground truth.
4.2. Experimental validation of the projective slice A first validation of the projective slice was carried out in a previous paper (Salom et al., 2005) in order to measure the impact of our technique during DP. This preliminary study showed that the project slice significantly contributes to error reduction. However, we obtained a number of errors due to a tradeoff between time and precision carried by the subjects that a geologist would naturally adopt in operational conditions. The protocol was changed to force the subjects to satisfy a strict constraint on the error rate to evaluate our technique only on the movement time metric.
4.2.3. Participants The participants were all volunteers and were not involved in activities related to steering tasks. We requested 16 subjects (10 male and 6 female) of an average age of 27.2 years. To avoid experimental variations generated by laterality, only right-handed subjects were selected. Same apparatus as the first experiment was used. 4.2.4. Variables The independent variable for the study of the visualization technique was composed of three modalities: static
ARTICLE IN PRESS P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
slice, dynamic slice and projective slice. Each subject carried out five tests for each picking case condition of treatment after the training period. The trial order of the modalities was selected randomly in Latin square. The dependent variables were the time of movement (ms) and the error rate (percentage), measured as the number of time that the cursor was more than 10 pixels away from the surface. 4.2.5. Results An ANOVA was applied to compare the mean averages of the error rate but provided no significant results (F(14,2) ¼ 0.822; p ¼ 0.452). However, we can observe that the error rate was very low with all three techniques. These results show that the subjects respected the protocol and adopted a conservative behaviour (less than 5% of error rate). The subjects were trained to increase precision instead of reducing their movement time. They made 1.2% errors with the static slice, 1.9% with the projective slice and finally 2% with the dynamic slice. We obtain significant results for the movement time with an ANOVA (F(14,2) ¼ 11.75; po0.05). T-Test for paired groups was applied to compare the mean averages of the movement time between each technique. The results are significant except for the comparison between the static slice and the projective slice (T(15) ¼ 1.944; p ¼ 0.071). The subjects obtained higher results with the static slice 7.467 s (s.d. 2.5) compared to the dynamic slice 9.420 s (s.d. 2.8) (T(15) ¼ 4.525; po0.001), which is consistent with our hypothesis. The projective slice permits a decrease in movements 8.171 s (s.d. 2.05) compared to the dynamic slice (T(15) ¼ 2.739; po0.05)) (see Fig. 9). The issues related to DP still remain due to the difficulties in anticipating the variation of the intersected surface in closed loop conditions compared to results obtained with the static technique. Subjects succeeded in reducing these disadvantages based on data previsualization. Using the projective slice, the subjects were both more precise and rapid compared to the dynamic slice. The
Fig. 9. Movement time (ms) for standard and projective slice (ms).
559
subjective sensations were noted, showing that all subjects preferred to visualize the surface with the projective slice rather than the dynamic slice. They felt more comfortable and could adapt their pointing to the variation of the surface to segment. From the operational point of view, it would seem that the projective slice could accelerate the time to point while preserving the criterion related to precision. It could moreover be used to eliminate numerous local ambiguities by providing 3D information on the global shape of a fault and of the entire fault network. 5. Discussion We performed a qualitative evaluation with six end-users to complete this experiment and it confirmed these results. We implemented our technique in the software used by geologists and asked them to segment the same fault using SP and DP. We measured the time spent by each expert to obtain a result that could be estimated as satisfying. Using a questionnaire, we gathered user judgments concerning the generated point cloud in terms of completeness and precision. The results showed that the total duration of picking in an operational context was reduced by 5 with DP compared to SP. The end-users also estimated the result to be more precise with SP and more complete with DP. All the users validated the point that animation improves and facilitates the localization of a fault. They also considered that our solution can facilitate cooperation and iteration between experts as it enables them to obtain a quick draft of a geological model. Our technique for surface recovery allows users to detect amplitude, texture or subjective contours. The dynamic picking is dedicated to tracking thin surfaces in any applicative context. However, for blob segmentation, our approach is not adapted and must be redesigned. Tracking closed edges with actual implementation leads to a sparse point cloud. In perspective, several applications need to extract a specific region of interest in a volumetric data without segmentating the whole volume. In medical applications such as arthroscopy, colonoscopy or aortic aneurysms for instance using Magnetic Resonance Images (MRI) or Computerized Tomography (CT) data, it is of interest to obtain spatial information on specific structures without occlusion or other problems linked to the 3D interaction. Some recent works, such as sketch-based methods (Owada et al., 2005, 2008), proposed, first, to realize a 2D rough sketch of the foreground and background regions and, second, to propagate the a priori to segment other following slices. The volume catcher system (Owada et al., 2008) proposes some implementations of special 2D interface tools for selecting a region of interest. The first module consists in a 2D image-processing module taking into account the user’s 2D gestures such as click, axis or free lines. The second one is the catcher module, which realizes the 3D subregion segmentation constrained by the a priori provided by the 2D area selected in the previous
ARTICLE IN PRESS 560
P. Salom et al. / Int. J. Human-Computer Studies 67 (2009) 551–560
step. The main drawback of the method is that the geometry of the 3D subregion deduced to the 2D module is constrained. Thus, we think that dynamic picking can be introduced to provide the user’s 3D gesture. 6. Conclusions In this paper, we have considered the specific problem of the manual segmentation or picking of underlying surfaces embedded in volumetric data. We have proposed a 3D path-based approach using animation of orthogonal slices to visualise and interact: the dynamic picking paradigm. There are two main advantages of the proposed method compared to the conventional 2D static picking technique: volumetric perception of the data and more rapid treatment of the volume. The animation of the data along a constrained path provides a method whereby an operator may continually analyse input textured data, allowing him to increase his perception and his conceptualisation of the tracked surfaces. Moreover, a multi-contextual technique of visualization, called projective slice, has been considered reducing the problems related to the dynamic aspect of the proposed method. Thanks to continuous navigation and projective slice, our conclusions, validated by experimental results, indicate the numerical signature of the surface produced by the operator using the dynamic pointing interface is characterised by an increased completeness for a substantial decrease of precision. In our opinion, pragmatic user-centered approach respects the expert’s work method while introducing new concepts that make easy a time-consuming and tiresome activity. Acknowledgement This work is supported by the Total Company. The content of this paper is the subject of the Total company patent (Keskes et al., 2006). The authors would like to thank Total company for the supply of the data. References Accot, J., Zhai, S., 1999. Performance evaluation of input devices in trajectory-based tasks: an application of the steering law. In: Proceedings of the Conference on Human Factors in Computing Systems, pp. 466–472. Boissonnat, J.D., Cazals, F., 2002. Smooth surface reconstruction via natural neighbor interpolation of distance functions. Computational Geometry: Theory and Applications 22 (1), 185–203. Carpendale, M.S.T., Cowperthwaite, D.J., Fracchia, F.D., 1997. Extending distortion viewing from 2D to 3D. Computer Graphics and Applications, 42–51. Cignoni, P., Montani, C., Scopigno, R., 1994. MagicSphere: an insight tool for 3D data visualization. Computer graphics Forum 13 (3), 317–328. Donias, M., David, C., Berthoumieu, Y., Lavialle, O., Guillon, S., Keskes, N., 2007. New fault attribute based on robust directional scheme. Geophysics 72 (4), 39–46.
Dorn, G., 1998. A modern 3-D seismic interpretation. The Leading Edge 17, 1262–1273. Drebin, R.A., Carpenter, L., Hanrahan, P., 1988. Volume rendering. Computer Graphics 22 (4), 65–74. Elmqvist, N., Tsigas, P., 2007. A taxonomy of 3D occlusion management techniques. In: Proceedings of the IEEE Conference on Virtual Reality, pp. 51–58. Fitts, P.M., 1954. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology 4, 381–391. Furnas, G.W., 1986. Generalized fisheye views. In: Proceedings of the Conference on Human Factors in Computing Systems, pp. 16–23. Guillon, S., Keskes, N., 2004. Sismage and the 3D visualization at total. In: AAPG International Conference. Gutwin, C., Skopik, A., 2003. Fisheyes are good for large steering tasks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 2003, pp. 201–208. Igarashi, T., Kadobayashi, R., Mase, K., Tanaka, H., 1998. Path drawing for 3D walkthrough. In: Proceedings of the 11th Annual ACM Symposium on User Interface Software and Technology, pp. 173–174. Jeong, W.K., Whitaker, R., Dobin, M., 2006. Interactive 3D seismic fault detection on the graphics hardware. In: Proceedings International Workshop on Volume Graphics, pp. 111–118. Keskes, N., Donias, M., Berthoumieu, Y., Guillon, S., 2006. Method and program for fracture plane reconstruction. United States Patent 20060190214. Kru¨ger, C., Tietjen, J., Hintze, B., Preim, I., Hertel, G., Strauss. 2005. Interactive Visualization for Neck-Dissection Planning. Eurographics 2005. Lorensen, W.E., Cline, H.E., 1987. Marching cubes: a high resolution 3D surface construction algorithm. Computer Graphics 21 (4), 163–169. Mackinlay, J., Robertson, G., Card, S., 1991. The perspective wall: detail and context smoothly integrated. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 56–63. McGuffin, M.J., Tancau, L., Balakrishnan, R., 2003. Using deformations for browsing volumetric data. In: Proceedings of IEEE Visualization (VIS), pp. 401–408. Owada, S., Nielsen, F., Igarashi, T., 2005. Volume catcher. In: Proceedings of the ACM Symposium on Interactive 3D Graphics and Games, pp. 111–116. Owada, S., Nielsen, F., Igarashi, T., Haraguchi, R., Nakazawa, K., 2008. Projection plane processing for sketch-based volume segmentation. IEEE International Symposium on Biomedical Imaging, 117–120. Ramachandran, V.S., Anstis, S.M., 1986. The perception of apparent motion. Scientific American 254, 102–109. Ropinski, T., Steinicke, F., Hinrichs, K., 2006. Visual exploration of seismic volume datasets. In: Proceedings of the 14th International Conference in Central Europe on Computer Graphics and Computer Vision, pp. 73–80. Salom, P., Becerra, J., Donias, M., Megret, R., 2005. Projective slice for a dynamic steering task. In: Proceedings of the ACM Symposium on Virtual Reality Software and Technology, pp. 138–141. Sethian, J.A., 1996. Level-set methods. Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision and Material Science. Cambridge University Press. Tory, M., 2003. Mental registration of 2D and 3D visualizations (an empirical study). In: Proceedings of the 14th IEEE Visualization, pp. 371–378. Treisman, A.M., Gelade, G., 1980. A feature-integration theory of attention. Cognitive Psychology 12, 97–136. Viega, J., Conway, M. J., Williams, G., Pausch, R., 1996. 3D magic lenses. In: Proceedings of the 9th Annual ACM Symposium on User Interface Software and Technology, pp. 51–58. Viola, I., Kanitsar, A., Groller, M.E., 2004. Importance-driven volume rendering. Proceedings of IEEE Visualization, 39–145.