MRI segmentation: Methods and applications

Magnetic ResonanceImaging, Vol. 13, No. 3, pp. 343-368, 1995 Copyright 0 1995 ElsevierScienceLtd Printed in the USA. All rights reserved 0730-725x/95 $9.50 + .oo

Pergamon

0730-725X(94)00124-3

l Review MRI SEGMENTATION: L.P. CLARKE,*

METHODS

AND APPLICATIONS

R.P. VELTHIJIZEN,* M.A. CAMACHO,* J. J. HEINE,* M. VAIDYANATHAN,* L.O. HaL,Jr R.W. THATCHER,~ AND M.L. SILBIGER*

*H. Lee Moffitt Cancer Center and Research Institute and University of South Florida, Department of Radiology, Tampa, FL 33612; TUniversity of South Florida, Department of Computer Science, Tampa, FL 33612; and tVA Medical Center, Neurology Service (151), Bay Pines, FL 33504, USA The current literature on MRI segmentationmethodsis reviewed. Particular emphasisis placedon the relative meritsof singleimageversusmultlspectralsegmentation,andsupervisedversusunsupervisedsegmentationmethods.Imagepre-processing andregistrationarediscussed, aswell asmethodsof validation. The applicationof MRI segmentationfor tumor volumemeasurements during the courseof therapy is presentedhereasanexample,illustrating problemsassociatedwith inter- and intra-observervariations inherent to supervisedmethods. Keywords: MRI segmentation;Image registration; Surgery simulation.

CT or simple MRI datasets.’ However, the differentiation of tissues within the tumor bed that have similar MR characteristics, such as edema, necrotic, or scar tissue, has proven to be important in the evaluation of response to therapy, and hence, multispectral methods have been proposed. 2*3 Recently, multimodality approaches, such as PET and fMR1 perfusion studies using radiotracers or contrast materials,4*5 have been suggested to provide better tumor tissue specificity and to identify active tumor tissue, for example, in recurrent tumors. Hence, segmentation methods need to include these additional image data sets. In the same context, a similar progression of segmentation methods are evolving for the planning of surgical procedures primarily in neurological investigations,“8 surgery simulations and “electronic rehearsals,“9-11 or the actual implementation of surgery in the operating suite’2,‘3 where both normal tissues and the localization of the lesion or mass needs to be accurately identified. The methods proposed include gray scale single image segmentation and multispectral segmentation for anatomical images with additional recent efforts directed toward the mapping of functional metrics (fMR1, EEG, etc.) to provide locations of important functional regions of the brain as required for optimal surgical planning. Similarly, in recent work on evaluating fMR1

1. INTRODUCTION

Recent advances in MRI system design have resulted in significant improvements in the areas of anatomical, functional, and dynamic imaging procedures. The generation of new 2D or 3DFT RF pulse sequences, such as fast spin echo (FSE), fast gradient echo (FGE), echo planar, and high speed gradient imaging methods provide an increasing array of multispectral data sets that contain unique features important for tissue segmentation and classification. Similarly, multimodality imaging methods (MRI, MRA, X-ray CT, PET, and SPECT) provide a means for correlation of anatomical and various functional metrics associated with each imaging modality. In this context, therefore, MRI segmentation is becoming an increasingly important image processing step for a number of areas that include: (a) identifying anatomical areas of interest for diagnosis, treatment, or surgery planning paradigms, (b) preprocessing for multimodality image registration, and (c) improved correlation of anatomical areas of interest with localized functional metrics. MRI segmentation has been proposed for a number of clinical investigations of varying complexity. Measurements of tumor volume and its response to therapy have used image gray scale methods as applied to X-ray RECEIVED and ACCEPTED 9/14/94. Address correspondence to Laurence P. Clarke, PhD,

Professor

of Radiology

and Physics, Department

diology, University of South Florida, College of Medicine, 12901 Bruce B. Downs Boulevard, Box 17, Tampa, FL 33612-4799, USA.

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response to cognitive paradigms, various metrics such as cerebral blood volume, blood flow, and blood oxygenation need to be correlated with segmented regions of the brain or alternatively with functional metrics derived from other modalities (PET, MEG, EEG, etc.).14,i5 Other applications of MRI segmentation include the diagnosis of brain trauma where white matter lesions typical of diffuse axonal injury (DAI), a signature of traumatic brain injury, may potentially be identified in moderate and possibly mild cases. These methods, in turn, may require correlation of anatomical images with functional metrics to provide sensitive measurements of brain trauma. MRI segmentation methods have also been useful in the diagnostic imaging of multiple sclerosis (MS),16 including the detection of lesions’7-19 and the quantitation of lesion volume using multispectral methods.20~2’ The application of MRI segmentation methods poses a number of significant problems. For example, the optimal selection of features in multispectral MRI is important to maximize tissue contrast differentiation or segmentation in feature space, while minimizing the computational complexity when they are used. The level of operator supervision in the segmentation process will impact the stability of the segmentation methods, particularly in terms of inter- and intra-observer variation. Accurate classification of the tissues that are segmented is important for both diagnosis and treatment strategies. The advances in dynamic imaging or fast imaging methods will require more attention to image noise suppression methods prior to application of segmentation methods. RF nonuniformity that results in image shading and is RF coil- and patient-dependent needs to be addressed to obtain accurate segmentation across the full field of view of the coil. An objective method is required to verify the results of the proposed segmentation methods, particularly for applications in therapy and surgery simulation, in the absence of direct knowledge of ground truth. Finally, multimodality approaches proposed for improved tissue specificity or for additional functional metrics pose significant and practical problems both in verification of image registration and segmentation as well as in the computational complexity of the algorithms involved.

The theoretical basis for MRI segmentation methods has been previously reviewed by these investigators. 22 The scope of this review is to address the practical problems of the implementation of MRI segmentation methods as outlined in the following sections. In Section 2, we will review current segmentation methods, both supervised and unsupervised as well as tissue classification methods. Section 3 will consist of a discussion on image preprocessing that includes noise suppression, edge and contrast enhancement, and RF uniformity correction. In Section 4, we will address the difficult question regarding validation. A discussion of multimodality registration methods related to segmentation is given in Section 5. We conclude with comments on the future directions for MRI segmentation and its applications in Section 6. 2. MR IMAGE

SEGMENTATION

2. I. Introduction Figure 1 shows a representative diagram of the most common parts of a computer vision system.23 Preprocessing improves the quality of the data by reducing artifacts, and will be addressed in Section 3. Feature extraction and selection provides the measurementvectors on which the image segmentation is based, and is covered in section 2.2. Segmentation groups pixels into regions, and hence defines the boundaries of the tissue regions and is described in Sections 2.3 and 2.4. Segmentation is followed by classification or labeling of the regions into the tissue types and is reviewed in Section 2.5.

2.2. Feature Extraction Segmentation of MR images is based on sets of features that can be extracted from the images, such as pixel intensities which in turn can be used to calculate other features such as edges, and texture. Rather than using all the information in the images at once, feature extraction and selection breaks down the problem of segmentation to the grouping of feature vectors.23* 24 Selection of good features is the key to successful segmentation.

Pixel intensity-basedfeatures. Many segmentation approaches reported in the literature simply use the

Fig. 1. Components of an image analysis system.

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gray scale values of the pixels.2~3,2’825-32The pixel intensity input can come from a single image,33,34 a volume data set,35-37 or a multispectral data set of the same anatomical location. Multi-spectral datasets typically consist of images acquired with a multi-echo sequence,21,26,2’,30-32,38 or images from multiple sequences.2*3,28,29If more images are available, they can simply be added to the feature vectors to form an N dimensional feature space. For example, new RF pulse sequences such as FSE and FGE, chemical shift imaging, and perfusion and diffusion imaging can provide additional features.15,22*39Multimodality imaging also provides additional pixel intensity based features potentially useful for improved segmentation,40 provided image registration can be successfully implemented (Section 5). Calculatedpixel intensity-basedfeatures. The use of calculated MR parameters has been proposed as features for image segmentation.4’-43 The increased noise generated in their nonlinear computations, however, have limited their application to MRI tissue classification.42 Similarly, the sensitivity and specificity of these calculated (absolute) parameters, for example, for the differentiation of normal and pathologic tissues and the reproducibility of these calculated metrics, has posed further problems in obtaining stable segmentation.42P44 Recent work has therefore generally focused on the use of these calculated metrics for classification of tissues in operator-defined regions.45-48 However, these features are inherently less sensitive to image nonuniformity compared with pixel intensity-based methodsa (Section 3). Their use for MRI segmentation may increase with the introduction of more efficient techniques for MR parameter estimation using low flip angle volume imaging methods with short imaging times (private communication with Dr. Marc Haacke, February 19, 1993). The use of other calculated MR parameters as features for segmentation are, in principle, possible with the advances, for example, in dynamic MRI methods. These include calculated metrics relating to flow of contrast material, cerebral blood volume, blood flow or blood oxygenation, and the use of such parameters is an area worthy of further investigation.4~s~1sFinally, transformations can be applied to any known vectors to generate new features. However, linear transformations such as the Eigenvector transformation to diagonalize the covariance matrix of the data may not improve the segmentation method, as they do not change the distances between feature vectors.** Edges and texture-basedfeatures. Some researchers have operated with only a single 2D or 3D MR gray scale image. Due to the nonuniform nature of MR im-

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ages (Section 3), a global threshold cannot segment MRI data, and additional features are required. Some researchers have used edge detection methods.33,49*50 In this area the use of the Marr-Hildreth operator (convolution of the image with 2nd derivative of a Gaussian) has been reported, 33,49although it is known not to have optimal localization. 49~51,52 Improved edge detection may be achieved using the Canny edge detector,53 but it is unlikely that methods based on edge detection will allow reliable segmentation.52s54 Another feature reported in the literature on MR image segmentation is texture.49s55T56Similar to the reported use of calculated parameters, texture features are mainly applied for classification, rather than delineation, of regions. 55s6Texture is a statistical feature necessarily derived from a large number of pixels (e.g., see Ref. 57); hence it is not suited for pixel classification.49 An alternative to statistical textural features has been reported. 58 The pixel intensities in a neighborhood were used as additional features; for example, each pixel forms with its eight nearest neighbors a 9-element feature vector. Although the approach appears promising, no reports are available as yet on their effect on the quality of the segmentations. Feature selection. Feature selection becomes important if the dimensionality of the data is posing computational burdens. There is one report of selection of image features,” but the work was restricted to the use of pixel intensities. Other investigators have reported on the selection of textural features for classification of regions, and found a small set that allowed labeling of regions of interest. 55 A large number of textural features for pixel classification has been reported and upon which discriminant analysis was performed to select the most relevant features.“9 However, generally there are not enough features available to pose a computational burden, or even allow robust segmentation. The criteria for feature extraction and selection has not been extensively studied but will become an important area as the number of features increases due to advances in MR imaging methods. 2.3. Gray Scale Single Image Segmentation The literature on MR image segmentation can be roughly divided into two categories: a single image, or gray scale, segmentation, where a single 2D or 3D image is used, and multi-spectral image segmentation where multiple MR images with different gray scale contrasts are available (Fig. 2). Single image segmentation methods can be subdivided as follows, and is illustrated in Fig. 2. Thresholding-based segmentation methods. The most intuitive approach to segmentation is gIobal thresh-

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Fig. 2. A classification of segmentation methods in literature.

olding, which has been performed on X-ray CT data. One common difficulty with this approach is determining the value of the thresholds. Interactive (operator) selection of thresholds for the intra-cranial region has been reported.60 Knowledge-guided thresholding methods, where global thresholds are determined based on a “goodness function” describing the separation of background, skull and brain have been proposed.34 The method is limited, and successful application for clinical use is hindered by the variability of anatomy and MR data, as well as image artifacts (Section 3). Local thresholding has been tried in other modalities,61 and has been applied to MRI data in combination with morphological filtering.62 Problems similar to global thresholding are anticipated, which have stimulated interest in the application of other methods.

Edge-basedsegmentation methods. Edge detection schemes suffer from incorrect detection of edges due to noise, over- and under-segmentation, and variability in threshold selection in the edge image.52 Bomans et al. combined edge detection (the Marr-Hildreth operator) with morphological filtering.33 The method required manual labeling and editing of the regions to generate satisfactory 3D displays. A similar approach is boundary tracing. 35 Ideally, the operation is as follows: an operator clicks a pixel in a region to be outlined, the method then finds a point on the boundary of the region and follows the boundary from that point. Several ways are open to the operator to optimize the result for nonideal cases. Other investigators describe a boundary tracing method using dynamic programming for noisy brain sections with vague boundaries,50 but successful application for global segmentation of MR images remains to be demonstrated since a good

initial guess for the boundaries is required. Boundary tracing methods in general, however, are likely to be restricted to segmentation of large, well defined structures such as the brain parenchyma, but not to distinguish individual tissue types.

Seedgrowing segmentationmethods. The literature is quite sparse on seed growing for MR images, but the method is commercially available with the program ANALYZE developed by the Mayo Foundation4 and the Allegro system (ISG Technologies, Toronto, Canada), both of which use seed growing for medical image segmentation and 3D reconstruction. Some early work on seed growing was described by Cline et al., who used seed growing to extract the brain surface.’ The segmentations require an operator to empirically select seeds and thresholds. Pixels around the seeds are examined, and included in the region if they are within the thresholds, sometimes adding the requirement that they are sufficiently similar to the pixels already in the region. Each added pixel then becomes a new seed whose neighbors are inspected for inclusion in the region. Some researchers have used seed growing or related connectivity algorithms to operate on segmented data as a postprocessing step to reduce “noise” in the segmentations and improve the appearance of the 3D reconstruction (e.g., see Ref. 26). Results obtained with seed growing are generally dependent on the operator settings. As with all single image methods, in practice only well defined regions can be robustly identified. Other segmentation methods. An associative memory technique has been reported, which appears to be very powerful. s4 Its practical application may be limited due to the extensive work necessary to acquire and to transform the geometry of normals in each slice to

MRI segmentation l L.P.

match each other, and its limitation to a particular type of MR acquisition. In the case of complex pathology, this technique only identifies areas of abnormality, and hence could fail to identify the boundaries between pathology and normal tissues. Random field methods have been successfully applied in other modalities.63*64 These methods include simulated annealing and iterated conditional modes; a review of these methods can be found elsewhere.65 Also reported is a Markov Random Field penalty to fine-tune a parametric classifier.28 Random field approaches require an energy function, which is often very difficult to define, that describes the problem. These approaches are also computationally intensive, which may prohibit their practical use. Summary. Gray scale segmentation methods may provide some useful information, but they are generally limited to relatively simple structures. For complex pathology, more information is often required, which is available in multispectral MRI data. 2.4. Multispectral Segmentation The most common approach for multispectral MRI segmentation is pattern recognition, the theoretical basis of which has been reviewed by these investigators.22 These techniques generally appear to be successful particularly for brain images, but much work remains in the area of validation (Section 4). Most publications describe the application of these methods to normals ,3,25,28*30while some researchers have looked at neuro-psychiatric disorders where the MR parameter distributions have the same characteristics as those for normals.21~27*31 More difficult problems are encountered with the segmentation of complex pathology such as that found in glioblastomas with associated tumor necrosis, edema, radiation necrosis, and tissue changes due to chemotherapy.3,22925@ The difference between supervised and unsupervised methods is stressed in the published literature due to the need for reproducible measurements. Although the distinction is not unique to multi-spectral approaches, the following sections will adhere to this formalism. Supervised methods require operator input for segmentation. This can be done by selecting training pixels or training regions on the images.2*3y30 It is useful to mention at this point that there is a recent report of automatic training-data acquisition.67 The method attempts to propagate training data from slice to slice, thereby minimizing operator interaction. However, the described method is still supervised as an operator selects the initial training set on an operator picked slice. Unsupervised methods are automatic, in the sense that operator intervention may be necessary to complete the process, but the result should be operator independent. Figure 3 shows the difference between the two ap-

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proaches. The input data consists of the features selected in the preceding step. An unsupervised segmentation method defines regions in the image without operator input. However, these regions do not have an anatomical meaning associated with them. Therefore a classification step is necessary to come to a labeled output image. 22 Classification is addressed in Section 2.5. Supervised segmentation methods. Pattern recognition methods. Supervised methods include several pattern recognition techniques. Many pattern recognition methods are assuming particular distributions of the features, and are called parametric methods. For example, the maximum likelihood (ML) method commonly assumes multivariate Gaussian distributions.2,3*26*30,31*68-70The means and covariante matrices for each of the tissues are estimated from a user supplied training set, usually found by drawing regions of interest (ROI) on the images. The remaining pixels are then classified by calculating the likelihood of each tissue class, and picking the tissue type with the highest probability. Parametric methods are only useful when the feature distributions for the different classes are well known, which is not necessarily the case for MR images.3 Nonparametric methods, such as k nearest neighbors (kNN) do not rely on predefined distributions, but on the actual distribution of the training samples themselves.3,21,31T71kNN has given superior results both in terms of accuracy and reproducibility compared to parametric methods.3 Also reported is the use of artificial neural networks3,25,72,73 and of a decision tree approach (ID3).58 Algebraic methods. Outside the field of pattern recognition, algebraic approaches have been reported.27*74-79 For images with clearly identified signature vectors these methods provide an elegant solution to deal with the partial volume effect, which may have some influence on volume measurements. Algebraic approaches, however, may become impractical for images showing complex pathology such as those described above. Since these methods work with projections of feature vectors, the number of more or less uncorrelated features that needs to be acquired to determine the eigenimages for every tissue becomes very large, potentially leading to impractical imaging times. A possible solution for the dimensionality was presented recentlyY9 However, these methods are optimal only for signature vectors that are more or less orthogonal, which may not be the case for pathologic tissues that exhibit similar relaxation behavior. Manual feature space segmentation methods. Other supervised segmentation methods include feature spacebased segmentations that utilize operator defined decision boundaries!2,80x81 Researchers have empha-

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Unsupewis8d segmentation

b

a. Unsupervised segmentation

b. Supervised segmentation Fig. 3. Unsupervised versus Supervised Segmentation. (a) Unsupervised segmentation: one or more input feature images are segmented into regions first, and then labeled with tissue types. (b) Supervised segmentation: labeled training data are provided first. After segmentation, these training labels are used to associate the labels for the entire image.

sized the feature extraction and selection step, and display these features in some multi-dimensional graph. The areas of higher density (clusters) that are visible in this graph are then manually outlined and associated with tissue types. Just et al. impose an ellipsoid containing a predefined fraction of known samples to delineate the decision boundaries,42 while other investigators employed a nearest-centroid classifier where the centroid is defined as the mean of an ROI in feature space. *’ Also reported is the manual selection of thresholds in each feature for each tissue type, forcing clusters into rectangular boxes.80 However, clustering techniques for unsupervised segmentation are clearly superior to these manual methods. Variability of segmentation methods. All supervised methods are operator dependent. Inter- and intraoperator variability has been measured and shown to be relatively large. 3*30,66An example of intra-operator variability is shown in Fig. 4.3 An operator selected training data for the same MR image data set on five

different occasions. The resulting five segmentations are compared in the graph in terms of the variability in the classification of individual pixels, evaluated for each tissue class. Shown is the percentage of pixels that are assigned to a particular class compared with the number of pixels that is assigned to that class at least once. Hence, the graph represents the maximum intraobserver variability for that data set. The issue of variability is further discussed in Section 4 on validation of segmentations using reproducibility criteria. Results to date by various investigators suggest that unsupervised methods are required for successful application in the clinical environment. Unsupervised segmentation. Unsupervised techniques, also called “clustering,” automatically find the structure in the data. A cluster is an area in feature space with a high density. This can be mathematically developed in different ways.82 Unsupervised methods employed for MR image segmentation include k-

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Fig. 4. Intra-operator variability for the three supervised kNN, ML and ANN methods. Results show the variations of five independent observations for normal brain tissues.

means 29*30,69,83 and its fuzzy equivalent, fuzzy c-means (FCM;. 25*32,84A comparison of two approaches has been reported by Gerig et a1.30:ISODATA, which is an extension of k-means to automatically detect the number of clusters (tissues), compared favorably to a feature-space approach where points of high density are matched to a table of tissue cluster centers. In an earlier publication, the latter method was shown to depend even on subtleties in MRI performance characteristics,85 and does not appear to be suitable for robust segmentation. In recent work a variant of the k-means algorithm was compared with two implementations of FCM.83 It was found that unsupervised clustering shows promise for tumor volume determination, but that initialization is very important for meaningful clustering results and reduction of computation times. Other researchers have described the application of FCM for volume measurement of normal tissues in hydrocephalic children that results in classification of partial volumes,32 allowing a meaningful interpretation of the fuzzy membership grades. It is not clear if this interpretation can be extended for larger numbers of clusters. These investigators compared two different implementations of FCM with a neural network, and showed that for normals the clustering appeared to be superior, but for pathologic cases more research is needed to obtain stable segmentations using only FCM methods.25 From a viewpoint of reproducibility, unsupervised methods are clearly desirable. However, unsupervised methods do not necessarily arrive at meaningful seg-

mentations, and often require long computation times.32,83*86 To overcome some of these limitations, Bensaid et al. developed a promising fully unsupervised method that uses a validity-guided reclustering algorithm, whereby low-quality partitions are iteratively refined toward better ones.*’ Another promising development, one which allows partial supervision, has been implemented as a semi-supervised fuzzy c-means (sFCM).~~ Initial testing of this algorithm showed that it reduced the operator dependency for tumor volume estimation compared to k nearest neighbors, while consistently arriving at anatomically meaningful results.66 It is possible, that in the future, clinically useful results with sufficient reproducibility and low requirements on operator time and skill may be achieved using partially supervised techniques, as the operator input guides an algorithm to detect structure in the underlying data. These partially supervised approaches are worthy of further investigation. Figure 5 shows results of some of the segmentation methods mentioned. Three images, the multispectral MRI dataset, of one slice of a patient with glioblastoma multiforme treated with radiation and chemotherapy are shown in Figs. 5a-c. It can be seen in Figs. 5d-i that unsupervised segmentation methods do not always converge to anatomical tissues. For example, in Fig. 5g, FCM did not separate CSF and edema. Two methods, partial supervision and validity-guided reclustering, are proposed to remedy this situation and their results are shown in Figs. 5h and 5i, respectively. Figure 5e shows a kNN result in which the white matter has been un-

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Fig. 5. Examplesof the resultsfor different multi-spectralsegmentationmethodswith correspondingraw MR imagedata. (a-c) Raw Tr weighted,PD, and T2 weightedMR images,(d) manually (hand drawn) labelledsegmentation,(e,h) supervised segmentationusingkNN and sFCM, respectively,(f,g,i) unsupervisedsegmentation(clustering)usingk-means,fuzzy c-means (FCM), and FCM reclusteringbasedon cluster validity, respectively. (Figure conlinues)

derestimated, most likely a result of the high operator dependencyof this method. A 3D reconstruction of this

dataset after k-means segmentation is shown in Fig. Sj. The volumetric data of this study is used in Fig. 6.

Domain-specific additions. Segmentation methods can be potentially improved when the segmentation algorithm is supplemented with & priori knowledge about the image. ** Methods can be optimized for the domain

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reconstruction of tumor, ventricles, and head.

by insertion of implicitly coded heuristics or by use of an explicit knowledge base. Often the knowledge used in MRI segmentation is acquired mainly by using training data rather than through interaction of a knowledge engineer with an expert, i.e., a radiologist. The most important feature used in heuristics and knowledge bases in the literature on MR segmentation is the image gray leve1.30*34,38,89 Also used have been the size, position and shape of regions,38T89,90 as well as anatomical constraints on neighboring regions.38’90.91 Implicitly coded heuristics are included in some presegmentation methods to extract the intracranial region.32,54,73These presegmentations are based on some knowledge about head anatomy, such as the fact that the T2 image shows a bright band of cerebrospinal fluid around the relatively dark parenchymal mass, or the morphometric properties of the brain. Other inves-

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tumor (red) has pushed the right ventricles

tigators describe an algebraic method for segmentation of the brain whose theory is based on properties of the MR image nonuniformity.27 The application of this method is restricted to mostly simple images.92 Some authors have inserted knowledge in the form of some average data. For example, a clustering technique has been reported that relies on a table of cluster centers for known tissues and simplified properties of the distributions of intensities for tissues.30 Also reported is the use of a template to describe a norma1.54,58P64This template is generated from a set of MRI scans of normals, geometrically transformed to match each other. These approaches may require specific image acquisition protocols and are likely restricted to the use of particular MRI systems. This approach has been taken one step further to a probability mask for various normal tissues in the brain.58 This atlas is no longer limited to

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specific protocols and MRI systems, and may find a broad use in MR image processing. This direction is certainly worthy of further investigation. Rather than encoding heuristics in computer program text, knowledge can be provided to the segmentation algorithm explicitly, for example, using a rule base.3*,89-9’ The advantages include the readability of rules; it is quite easy to understand a rule compared to a piece of C-code. Also, the rule base can be changed easily and constructed incrementally; possibly learning can be done with a rule base.91 However, rule bases have been mainly used for labeling of regions in segmented images, as described below.

2.5. Classification Image segmentation refers to the decomposition of an image into regions, but strictly speaking does not classify the tissue types. Classification is the step that deals with the labeling of the regions. In supervised segmentation, this is done by an operator when labeling training data, where pixels are associated with anatomical tissues (Fig. 3). Unsupervised methods return a segmentation with different “classes,” but the association of classes with tissue types must still be performed, as is shown in Fig. 3. Most commonly, this association is done by an operator, or implicitly by an interpreting physician. Some researchers used a region classification scheme using textural parameters.49 Its success was limited as some user interaction was necessary to correct classification errors even on normals. There are some reports of automatic rule based labeling.38,s9,90 Much of the knowledge used for labeling is based on the order of mean intensities of regions; for example, the darkest region corresponds to the background, and a region that is bright in Tz but dark in T, corresponds to cerebrospinal fluid. Other types of knowledge are captured in rules on shape. For example, Li et al. differentiate the extra-cranial tissues from brain parenchyma by looking at a poIygon enclosed by the inner region.89 Others have approached the labeling of extra-cranial tissues using the outer circular shape.” Automatic labeling may prove to be a very complicated step when no interaction with an operator or radiologist can take place. However in clinical practice, the intervention of an operator to manually label the regions in the segmented volume to allow calculation of its geometric measures does not appear to pose problems. Automatic labeling may find its use particularly in automatically detecting errors in the segmentation and improving the accuracy by knowledge guided resegmentation as reported by these investigatorsgo and this approach, in turn, is worthy of further investigation.

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3.1. Introduction Due to the inherent technical limitations of the MRI process, uncertainties are introduced into the MR image including: random image noise, spectral leakage or Gibb’s artifact,93 partial volume effects, and MR signal intensity variation induced by nonuniform radiofrequency fields (RF). A more complete and comprehensive coverage of the contributing sources of error inherent in MR images can be found elsewhere.94 The image preprocessing techniques reviewed here are mainly concerned with reducing the detrimental effects of the artifacts mentioned for the purpose of applying segmentation methods. When implementing a particular image processing technique there are some fundamental considerations to address. These include the amount of information loss, the SNR gain, edge and fine detail degradation, and to a lesser extent computational complexity and time. It may be the case that optimizing one of these compromises the others. This analysis reviews some well understood conventional linear approaches for noise removal and contrast enhancement, and newer locally adaptive filtering approaches as well. The specific techniques for noise removal and contrast enhancement include linear low pass filtering, image combination analysis, Bayesian analysis, scale space techniques including wavelet theory, maximum entropy methods (MEM), and adaptive nonlinear filtering. Techniques for RF nonuniformity corrections discussed include correction matrix approaches, digital filtering, curve fitting techniques, and statistical methods. 3.2. Noise Suppression and Contrast Enhancement Linear low pass filtering. Conventional linear low pass filtering of MR images has been previously investigated. 93,95,g6Low pass filtering reduces noise relative to the true signal intensity in regions of the image where the spatial features vary slowly. However, edge definition is not maintained, and a fair amount of blurring and loss of fine detail is encountered. The cost incurred in reducing noise and gaining contrast resolution is the loss of spatial resolution. 93 For an explication of the resolution/signal-to-noise ratio (SNR) trade-off problems with MRI and the information content of MR images, see Ref. 97. Fine detail may be lost and this can potentially lead to erroneous segmentation results. Combination analysis. A variation of linear matched filtering has been utilized for contrast-to-noise ratio (CNR) enhancement. 98 This type of process is an extension of a class of enhancement techniques referred to as linear image combination analysis.99 When sev-

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era1 two dimensional scans of the same slice are acquired, the filter output will produce an image with greater CNRs between diseased and normal tissue than that in any of the originals. This method is contingent upon defining two regions of interest in which the CNR is to be optimized prior to filtering. The strength of the filter is in visual improvement and not in aiding detection, hence some prior knowledge of pathology must be known. A more complete comparative analysis of CNR methods is reviewed elsewhere.100 This type of analysis does not necessarily lead to more accurate segmentation of the desired features or correct for partial volume effects.“’ Adaptive filtering. The MR magnitude signal distribution has been investigated,“’ and later extended to power images for correction purposes.‘02 However, when considering local regions or groups of pixels, the resulting distribution can appear as uniform (no distribution tail), Gaussian (middle tailed distribution), double exponential (long tailed distribution), or any type of distribution in between. This varying distributional behavior has been investigated in our laboratory using statistical identification criteria given by Hogg,lo3 and tolerances given by Restrepo et al.lm This investigation indicates that the local signal distribution kurtosis may vary widely over the spatial location in MR images. Recently some promising locally adaptive techniques have been applied to MR images.105-‘13 These methods include: Bayesian processing, noise filtering and edge detection with wavelet transforms, nonlinear anisotropic filtering, V filtering, and iterative adaptive nonlinear filtering. In addition, we are currently examining adaptive ordered statistical methods. Bayesian image restoration. Bayesian image restoration formalism has been adopted to process two dimensional MR scans.“’ The stated findings indicate that the process performs reasonably well for noise reduction, ringing artifact removal, and edge enhancement. However, spurious artifacts can appear in the reconstructed image if the proper initialization parameter is too small. Conversely, inadequate intra-region smoothing along with edge blurring is encountered if the initialization parameter is too large. In either case, long computation times are needed for processing. Therefore, the three dimensional extension of this filter may not be plausible at this time. Wavelet analysis. Wavelet analysis has become an important image processing technique.‘r4,‘” Due to the scale-space properties of the transform, the wavelet coefficients contain local information, a property which renders itself useful for edge detection,“’ wavelet coefficient amplitude reduction,ic”j and amplitude cutoff

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thresholding ‘12 for noise removal purposes. Johnson et al. presented a comparative analysis of median and Gaussian filtering vs. an adaptive wavelet method for noise removal in echo planar time series imaging and found the wavelet method to be superior.‘13 Takaya used the first derivative of a Gaussian function as the wavelet for edge detection and enhancement of such images. lo7 We have also used wavelet methods at our institution with success in other imaging modalities’ 16,1“,lB4 and recently for enhancing fine detail and edges in MR images in conjunction with adaptive ordered statistical filtering for noise removal. The coefficient amplitude reduction technique introduced by Weaver et al. is quite effective in reducing the noise while maintaining edge definition.‘06 An attractive feature of this process is that the gain values can be set with spatial dependencies. However, the reported findings also indicate that some fine detail is lost, some local edge artifacts are introduced, and the manner in which to set the coefficient reduction values is unclear. Maqbool used wavelet coefficient thresholding in functional MRI (fMR1). The thresholding technique is implemented by truncating all wavelet coefficients below a particular cutoff value; this denoising method is effective in that the noise is removed while preserving the fMR1 signal characteristics. A promising analytical method of suppressing wavelet coefficients for noise removal”’ has not yet been implemented for MR images. This method uses a singularity criteria for coefficient suppression based on how wavelet coefficient amplitude extrema transcend scale. These image enhancement techniques appear visually appealing with reduced noise and sharpened edges. However, the impact these methods may have on automated segmentation has yet to be established. These methods may prove important for fast imaging methods which have increased noise content and/or limited image contrast. Anisotropic diffusion filtering. Another scale space adaptive technique introduced for noise reduction and contrast enhancement is postulated on the discrete solution of the anisotropic diffusion equation, and this results in an iterative approach”’ (for the motivation see Ref. 120). The paradigm of the filter design is quite amenable to the goals of MR image enhancement: (a) causality, that is to say that no spurious artifacts are generated from fine to coarse scale, (b) immediate localization, in which the boundaries sharpen at each resolution, and (c) preference of intra-region smoothing to inter-region smoothing. When implemented on MR images,lo8 the reported results are in accord with the filter design paradigm: increased SNR, intra-region smoothing, and edge sharpening. This method of enhancement can be used when the acquisition method

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is less than optimal (fast scans where SNR is reduced) including reduced slice thickness. Additional amenities of the process include computational speed and algorithm simplicity. The filter is designed to operate on image structures comprised of smooth homogenous or piecewise slowly varying regions separated by defined boundaries. This criteria is only partially true for MR images. Nonlinear adaptive filtering. Other adaptive methods include the iterative V-Filter technique,“’ iterations of adaptive nonlinear filtering,“’ and adaptive ordered statistical filters.‘11~116~117The V-Filter approach uses a local variance criteria for the adaptive filtering. It is claimed that the technique will provide boundary enhancement and iteratively reduce noise. The published results indicate that the filter is useful for large tumor boundary detection. The reported SNRs are impressive, however the convention used for the SNR calculations is not in accord with the accepted MR protocol,121 and no assessment of fine detail loss is given. Adaptive iterative filtering, deemed “mode-change-filtering,” ‘lo is a two-step process in which the first mode iteratively reduces small amplitude noise and the adaptive second mode iteratively reduces residual small amplitude noise as well as spike-like noise. SNR improvements of a factor of two and greater are reported and image sharpness is retained. The reported findings indicate that the iterative process may introduce spurious spike-type aberrations into the image. At our institution adaptive windowed median filtering 122and adaptive alpha trimmed mean filteringlo in conjunction with local contrast enhancement techniques 123 for smoothing and edge enhancement have been implemented. Our preliminary results indicate that substantial SNR gains and modest local uniformity increases are possible. Ying et al. introduced a method termed “adaptive filtering method with edge detection and noise estimator” (AFEN).“’ This technique uses edge detection in conjunction with a noise estimate to make the appropriate filter decision. AFEN was applied to high resolution, low SNR MR scans and produced SNR increases of a factor of 4.5. Summary. There is not much in the literature reporting on the positive or negative impact of noise filtering and image enhancement in relation to segmentation and this area requires further work. However, one study indicates that preprocessing (using the anisotropic diffusion method as described earlier) prior to segmentation improves inter- and intra-operator quantitative volumetric measurement reproducibility.‘” Although images may be visually more appealing, edges and sharp details may not be the optimal features to enhance. An alternative approach to enhancement might

be to selectively enhance specific features, and the band pass nature of wavelet analysis may be of some benefit. 3.3. RF Nonuniformity Image nonuniformity may be caused by a number of factors that include: RF transmitter and receiver inhomogeneities, RF penetration, and static field inhomogeneity. 125Segmentation accuracy is dependent on the quality of data, and the greatest limitation of MR data is image nonuniformity due to the inhomogeneous RF field.126 Recently, a review of the sources of MR signal intensity nonuniformity has been reported.127 A number of correction methods have been proposed~125,126,128-135 Th ese methods fall into the general framework of correction matrix methods,125*126~128,133,135 digital smoothing and filtering,126~128~129~132~133 combinations of correction matrix and digital smoothcurve fitting,125*134 polynomial modeling,136 ing, 130~131 statistical methods,13’ and perhaps Maximum Entropy Methods (MEM).138 Correction matrix. When implementing the correction matrix approach, the coil sensitivity profile is found from the image of a phantom containing a uniform solution of MR contrast material. This is then divided into the original image. The method has the disadvantages of adding spatial dependence to the noise. Improvements to the correction matrix approach have been found by using two orthogonal correction matrices.135 However, the method assumes that RF penetration through the phantom and phantom coil loading is the same as the subject being imaged, which is not the case.126 Digital filtering. A digital filter is derived on the assumption that the true image is comprised of smooth background with high frequency detail superimposed. The coil sensitivity is found with the appropriate filtering. However, this method may not be used as a universal correction because the construction premise is not totally correct. An improvement over the correction matrix approach and digital filtering method combines aspects of both. 130,131This approach uses a low resolution body coil image to find the surface coil profile. The body coil image is smoothed and the reciprocal of the surface coil profile is found by dividing the smoothed body coil image by the smoothed surface coil image, smoothing with median filtering and then multiplying by the image. This technique has a disadvantage in that extra time must be spent acquiring the low resolution image, and the influence of the low SNR of the body coil image is not taken into account. Surface fitting. Curve fitting routines have be used to approximate phantom image pixel values as a func-

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tion of their position relative to some reference value, with which a corresponding correction matrix can be constructed.125 The results reported indicate that this is an effective method. Subsequent work has been implemented with curve fitting routines using patient images, and its associated merits relating to semiautomated segmentation.1’4 The findings indicate that the interpolation procedures have a positive impact on segmentation outcome. The polynomial modeling technique136 is closely related to the curve fitting routines, but differs in that a maximum variation second order polynomial is found using the MR image data. This polynomial is then subtracted from the corrupted data resulting in a compensated image. The reported findings indicate that regions with large uniformity corruptions are compensated, while regions exhibiting minimal uniformity artifacts are unaltered. Further work is required in this area. Statistical method. This novel approach uses knowledge of the tissue properties and intensity inhomogeneities to correct for the RF nonuniformity.137 The algorithm is termed “Expectation-Maximization” (EM), and it results in EM tissue segmentation and gain field estimation. The reported findings indicate white/ grey matter automated segmentation is enhanced by this method. The results of this work show promise as an acceptable method for correction of the RF nonuniformity. Maximum entropy method (MEM). Another uniformity correction technique is the Maximum Entropy Method (MEM). Applications of MEM in image processing, including tomographic and MR spectroscopy examples, are given elsewhere.‘39 Constable and Henkelman140 showed that the application of MEM in MRI for noise removal and artifact suppression is not satisfactory. However, Moran’38 reported that the definition of entropy given by Constable and Henkelman is suspect. Moran defines pixel intensity value as a transmitted variable and not by pixel location as defined by Constable and Henkelman. Moran’s findings indicate that MEM has strong potential for further applications in MR. Further investigation is warranted in this area. Summary. RF uniformity corrections are essentially dependent upon the patient, slice orientation, RF coil design, and pulse sequence, and the method of choice should ideally be applied to the subject image. Surface fitting methods’34,136 as well as the statistical method137 appear to be reasonable approaches with good potential, considering each image is corrected on its own merits. These investigations in conjunction with adaptive nonlinear filtering and multiresolution tech-

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niques may be the best direction to follow in an attempt to reach an optimal solution for enhanced automated segmentation processes. 4. VALIDATION

4.1. Introduction MRI segmentation is being proposed either as a method for determining the volume of tissues in vivo or their 3D spatial distributions in applications involving diagnostic, therapeutic, or surgical simulation protocols. Some form of quantitative measure of the accuracy and/or reproducibility for the proposed segmentation method is clearly required. Since a direct measure of ground truth is not logistically feasible, or even possible with pathologic correlation, several alternative procedures have been reported for different clinical investigations. This section therefore briefly reviews some of the methods reported for tumor and normal tissue volume measurements. The methods for verification of surgical simulation have not as yet been reported in detail and further research is necessary in this area. The methods reviewed include the use of MRI contrast studies, phantom validation, MRI simulation studies, correlation with pathologic findings, reproducibility studies and, finally, manual labeling of MR images. 4.2. MRI Contrast Methods The use of MR contrast for tumor detection using SE T, -weighted sequences, although commonly used as a diagnostic indicator for tumor boundary definition, has some inherent problems. The use of MR contrast agents in neuroinvestigations of the brain provide information about whether or not a breakdown of blood-brain barrier (BBB) has occurred and on the integrity of the tissue vascularity both of which are often tumor type- and stage-dependent.14’-144 However, MR contrast may not be optimum for the quantitative differentiation of active tumor tissue, scar tissue, necrosis or edema, or for recurrent tumors. Many segmentation methods, in particular gray scale methods and multispectral methods, use MR contrast information with SE T1-weighted images for tumor volume or size estimations despite the limitations of these methods in the absence of ground truth determinations.59J45 As shown in Fig. 6, these segmentation methods yield different estimates of tumor volume, but similar changes during the course of therapy. Recently the use of multimodality imaging methods, such as the correlation with PET perfusion studies, have been proposed to identify active (perfused) tissues.4 Alternatively, the use of fMR1 measurement of contrast dynamics has been suggested to provide better differentiation of active tumor

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tissue in neurological investigations and these functional images could be potentially included in segmentation methods. 4,5 The measurement of active tumor tissue volume using segmentation methods and its response to therapy requires further investigation. 4.3. Validation Using Phantoms The use of phantoms constructed with compartments containing known volumes is widely reported.8,21,27*30-32,35*74,‘46 The typical phantom represents a very idealized case consisting of two or three highly contrasting classes in a homogenous background.27*35,‘46 Ph an t oms containing paramagnetic liquids and gels doped with paramagnetic agents have been introduced to mimic MRI parameters of the tissues being modeled. 8*2’*30 However, phantoms have not evolved to encompass all the desired features which would allow a realistic segmentation validation, namely: a high level of geometric complexity in three dimensions, multiple classes (e.g., representative of white matter, gray matter, cerebrospinal fluid, tumor, background, etc.), and more importantly, RF coil loading similar to humans and MRI parameter distributions similar to those of human tissue. While two studies came close by introducing a phantom with a more advanced level of geometric complexity in two dimensions and human brain-like MR parameters, these phantoms did not have the other required characteristics, and therefore did not allow for all sources of error in estimates for volume calculations.8*30

The reported accuracy obtained using phantoms is very high for large vo1umes,27*32,35but decreases as the volume is smaller.27*30 Table 1 reports the accuracies obtained in various phantom and other verification studies. Although they represent a highly idealistic case, phantoms display a wide range of accuracies dependent upon method and phantom of choice (Table 1). For a true indication of the maximum obtainable accuracy of the segmentation methods, the phantom volumes should be comparable to the anatomical or pathologic structures of interest. For instance, the measurement of the volume of the intra-cranial cavity is relatively simple and will not require very sophisticated segmentation techniques; but when the volume of multiple sclerosis lesions is measured for example, much higher demands on accuracy and reproducibility of the techniques will be necessary. In summary, phantoms do not fully exhibit the characteristics that make segmentation of human tissues so difficult. The distributions of MRI parameters for a given tissue class is not necessarily Gaussian or unimodal, particularly during therapy protocols, and will often overlap for different tissues. The complex spatial distribution of the tissue regions, in turn, may cause the MR image intensity in a given pixel to represent signal from a mix of tissues, commonly referred to as the partial volume artefact. Moreover, phantoms load the RF coils differently than human subjects do, with greater nonuniformity artifacts due to enhanced RF attenuation in the phantom material’33*‘34 (see also Sec-

Table 1. Comparison of accuracies reported for various phantom and other verification studies Author

Method

Type

Accuracy

Jack 1990

Manual tracing/thresholding

Phantoms

3%

Ashtari 1990

Operator-guided boundary tracing

Phantoms

2%

Kohn 1991

Linear decision boundary in 2D feature map

Phantoms

9%

Cline 1991

kNN

Phantoms

1%

Gerig 1992

Maximum likelihood

Phantoms

3%

Peck 1992

Eigen-image

Phantoms

Rusinek 1993

Eigen-image

Egg yolk Phantoms

2% 12%

Jackson 1993

kNN

Phantoms

2%

Vinitski 1994

kNN

Phantoms

9%

Mitchell 1994

kNN or maximum likelihood

Phantoms

10%

Snell 1994

Active template matching

Dissected brain cadaver

10%

5%

6%

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tion 3). Although phantom images provide an excellent means for daily quality control of the MRI scanner, they can only provide a limited degree of confidence in the reliability of the segmentation methods. In their present form, phantom measurements cannot realistically be used to clinically validate MRI segmentations but should be used instead to rule out invalid segmentations arising from variability in MRI system performance. 4.4. Validation Methods Using MRI Simulations With the advent of fast available computing the feasibility of studying MR phenomena via computer simulations is very attractive. Several MR signal simulations, and the resulting image reconstruction methods can be found in research literature.‘47-‘53 These simulation methods have so far not been used for the evaluation of segmentation methods, but were used to investigate a wide variety of MR processes including: optimization of RF pulse techniques,‘47*149*152the merits of spin warp imaging in the presence of field inhomogeneities and gradient field nonlinearities,‘48 the feasibility of replacing phase-encoding with wavelet encoding,i5’ noise filtering,i5’ and pulsed field gradient measurements in porous media.‘53 The simulation methods, as proposed by these investigators, usually involve numerical integral solutions of the problem at hand. If the object to be simulated is of any spatial extent the process can be quite time consuming. The more time efficient k-space formalism, originally developed to study MR pulse techniques154-‘56 and extended to study corrections to nonuniform sampling,157 has been incorporated into MR simulations.‘52 The phenomenon being studied quite often will dictate the simulation method used. The k-space formalism allows for parameter setting (e.g., T, , T2, and to a large extent the pulse sequence) modifications without the need for lengthy recalculations. However, the influence of nonlinear magnetic fields, including nonlinear field gradients and nonuniform RF fields, can not be handled in a straight forward fashion with k-space formalism.15’! In summary, simulation methods can be extended to include MRI segmentation analysis. The robustness of the segmentation process may be probed by corrupting the simulated signal with noise, nonlinear field gradients, or more importantly, nonuniform RF excitation. In this fashion, one source of signal uncertainty can be introduced at a time, and the resulting segmentation uncertainty can be related to the signal source uncertainty in a quantifiable manner. Usually in the simulation routine the designated relaxation parameters for a given tissue are constant values. A more realistic approach would be to treat the relaxation param-

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eters as distributions, which would give simulations an advantage over phantoms which have unrealistic relaxation parameters. 126Hence, we believe computer generated simulations may eventually provide a good way to quantify segmentation methods, and are pursuing this line of reasoning at this institution.‘5’ 4.5. Ground Truth: Correlation With Gross and/or Histopathology Attempts to correlate MRI segmentation data with gross and histopathology have been reported.29*37*84.‘45*‘58 Taxt et al., base the correlation merely on the identification of the tumor type in cases of uterine tumors.29 Other investigators compared the brain volume of a dissected brain, measured by immersion, with the volume found by a segmentation method.37 Similarly, measurement of the tumor volume of the core of tumors by immersion has been reported, and some correlation (r* = .77) with volumes from manual segmentation based on contrast enhancement.14’ Other investigations used histopathological findings to compare locations of lesions with those found by an automatic detection method.‘59 Comparison of segmentatation results using only the appearance of gross pathology has been reported.‘58 These investigators topologically correlated the segmentation results of a postmortem MR image set of a brain with gross and histopathology, and found good correlation for some aspects of the segmentation.84 Techniques that only compare volumes give a good first measure of accuracy but may not be adequate for validation of segmentations as they do not confirm the location or shape of the anatomy of interest, in particular, the spatial distribution of tissues within the tumor bed. There are also difficulties with morphometric metrics: premortem data may not correspond well to pathology because of the logistics of the excision; the MR relaxation behavior of excised tissue is very different from perfused organs; and the work is labor intensive. In conclusion, pathology correlations, despite their apparent value for ground truth and their relevance in tumor characterization may prove to be not logistically feasible as a method for verification of segmentation methods. 4.4. Reproducibility Studies Reproducibility has been reported as an important indicator for confidence in the segmentations?*21P27.30.35 Reproducibility has been measured under different variations: (a) using the same data, but having the same operator select several training data sets (intra-observer variation); (b) allowing different operators to perform the segmentation (inter-observer variation); (c) stabil-

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studies. Likewise, they are useful in determining the inherent variability due to the sensor response characteristics or between different scanners over the time period of the study. Repeat measurements of the brain tissue volume of normal volunteers can help evaluate the im4.0.104 i aging methods for artifact reduction such as partial *volume averaging” and uniformity,‘33 and time de- ISG-SG 5 pendent variation of the sensor. Reproducibility of sf : GT volume measurements that can be obtained with seg3 3.0*104kNN mentation methods using MR images of normal vol9 unteers is certainly more indicative of the reliability of : SFCM 8 the techniques than those obtained using phantoms. The reproducibility of the segmentation results may not be similar to MRI studies of normal volunteers compared to those of patients, especially ones treated with radiation and/or chemotherapy. For example, variations of 8% and upwards in lesion volumes have I I I I 1.0.104 I 1 been reported for inter- and intra-observer variations Initial Week7 Week13 Week16 Week20 for difficult cases,30,66*161 but absolute errors as found SCAN TIME by immersion techniques or manual labelling can be as much as 100%.2,66 Jackson et al. found that the reproFig. 6. Volume measurements for a glioblastoma multiforme tumor during therapy using kNN, sFCM and seed-growing ducibility for their method to estimate the volumes of (ISG-SG) methods, compared to ground truth (GT) obtained MS lesions was on the order of 20% .21They indicated by manual segmentation of the tumor. The shadowed area that these results would improve by using image preshown for ISG-SG, kNN and sFCM methods represent the processing techniques before segmentation. These instandard deviation of the repeat measurements made by independent observers (from Ref. 66). vestigators did an analysis of several methods and found inter- and intra-observer variation (reproducibility) better than 8% for their multi-spectral techniques, compared to a typical 15% variability found using seedity for multiple scans of the same subject using different growing,66 although the accuracy, calculated by comimaging sessions and (d) variations of the segmentation parison with the ground truth volumes obtained by over different patients.3 Reproducibility does not promanual pixel labeling, showed a systematic undervide a measure of accuracy, it merely gives a measure estimation by the multi-spectral techniques (Fig. 6). of reliability under the variation that is tested. Some A summary of published work reporting normal applications do not require accurate measurements, but tissue and lesion volumes is presented in Table 2. The rather reproducible values. For example, when monireproducibility of the reported measurements for nortoring the effect of radiation treatment, one is more inmal tissue volumes is dependent on the segmentation terested in the relative time related changes in tumor method, targeted times, and the size of the tissue volumes. Similarly, the reproducibility of the reported and edema volumes,@j as illustrated in Fig. 6. measurements for lesion volumes has a broader range Many validation studies have been based on the reproducibility obtained with MR images of normal, healthy and is dependent on lesion type, staging, and size. The use of a common database for comparison of segmenvolunteers to evaluate (a) the accuracy and reproducibility of the segmentation methods,‘9~27~70~71~75~‘5*~‘60~16’ tation methods and their reproducibility is required. (b) the effectiveness and correctness of image acquisition and image preprocessing techniques,75*133 (c) the 4.7. Manual Labeling of MR Images validity of automatic tissue labeling methods,89 and Some investigators have approached the validation (d) as controls in studies to determine subtle changes problem using experts to manually trace the boundaries in normal brain tissue volumes associated with many of the different tissue regions.2,38,72,‘62 The major adneurologic disease states such as brain trauma and Alzvantage of the manual labeling technique is that it truly heimer’s disease.31,32*‘6’ mimicks the radiologist’s interpretation, which realisThe stability of supervised and semi-automatic tically is the only “valid truth” available for in vivo imsegmentation methods with respect to operator depenaging. However, there is considerable variation with limiting “ground truth” determinations. dency in the selection of the training sets for the segoperators, 30*163 mentation algorithms may be evaluated using volunteer Furthermore, manual labeling is labor intensive and 5.0.104

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Table 2. Reproducibility results for various volume measurement studies, including both normal tissue and lesion volumes Author

Method

Tissue type

Intra Obs.

Inter Obs.

HF ATL Brain CFS Brain Brain CSF WM GM Brain CSF WM GM HF Amygdala Brain CSF

10% 3%

4% 16%

14% 6% 1% 6% 3%
MS lesion WM lesion MS lesion MS lesion MS lesion Edema (cats) Brain tumors

21% 20% 10% 10% 8%

34% 28% 21% 6% 5% 8% 8%

Inter

scan

Inter Voltr.

Normal TissueVolume

Jack 1990

Manual tracing/thresholding

Kohn 1991

Linear decision boundary in 2D feature map 2D feature map Maximum likelihood

Herman 1991 Gerig 1992a

Kikinis 1992

kNN

Bartzokis 1993

Manual region thresholding

Jackson 1993

kNN

2%

1% 8%

3% 3%

-

6%

6% 3%

15% 27% 17% 13% -

-

-

Lesion Volume

Herman 1991 Kikinis 1992 Jackson 1993 Narayana 1993 Vinitski 1994

2D feature map kNN kNN kNN kNN

Vaidyanathan 1994

kNN or SFCM

Obs. = observer; Voltr. = volunteer; HF = hippocampal formation; ATL = anterior temporal lobe; CSF = cerebrospinal fluid; WM = white matter; GM = grey matter; MS = multiple sclerosis.

currently cannot be feasibly performed for large numbers of image data sets. Improvements in the area of manual labeling may be found by interfacing locally operating segmentation techniques with manual improvements. Also, a confidence level could be attached to each pixel, possibly automatically as proposed by these investigators. 16xAs manual labeling allows an evaluation most closely related to the radiologists’ opinion, these improvements deserve further investigation. Furthermore, to aid in the validation and comparison of techniques, it would be helpful to establish a common database of images and their truth labels from institutions working on the MR image segmentation field. 5. MULTIMODALITY

REGISTRATION

5.1. Introduction Medical neuroimaging has evolved a high demand for the integration of the information derived from its

various modalities. l4 In response to this demand, registration techniques have developed for tomographic three and four dimensional analyses of human perception and cognitive function.58,‘64 Registration, the correlation of spatially related images, is a central process in the developing aspect of medical imaging. Often, registration is a multistep procedure, of which the actual alignment of spatial data (in essence the acquisition of the interscan coordinate transformation’65) is but one component. In some instances, segmentation is performed as a necessary prerequisite for registration.166 In other instances, segmentations such as those that rely on multispectral data initially require registered data. This review will focus upon registration techniques which may aid or benefit specifically from advancements in segmentation algorithms. More general image registration methods are reviewed elsewhere167*168 and more recentlyI in relation to functional neuroimaging. Image registration combines the spatial in-

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formation afforded by such modalities as MRI and X-ray CT, with functional data from SPECT, PET, or EEG such that the strengths of the combined methods summate while the weaknesses cance1.169,‘70 Images to be registered may be intrasubject (from a single patient, either from multimodality scans or from single modality serial scans170) or intersubject (from several patients from a single modality, as is required in the intersubject registration of data in standardized Talairach space,17i or with respect to a normative MRI database5*).

dalities, specifically in the area of nuclear medicine.i6’ Due to the relatively high resolution of MRI, the application of this method in intersubject or intrasubject MR image registration should be trivial. It can be postulated that further improvements in the automation and accuracy of segmentation would add to the ease and accuracy of the registration. The latter claim of accuracy would be and is a difficult metric to measure since this technique, as well as the other retrospective techniques, all suffer from the inability to provide a direct estimate of registration accuracy for a given case.165

5.2. Registration Methods There are roughly five categories of image registration techniques’72: physical head restraint, external fiducial markers, principle axes, surface matching, and internal anatomical landmarks. The first two methods are prospective in nature, in that they require a rigidly controlled setting in the acquiring of patient data.“j5 The latter three, which may be termed retrospective techniques, offer the advantage of being able to be used with previously acquired data and not requiring added control in the acquisition phase.‘65 These methods rely on being able to identify corresponding features on the images to be registered and then calculating the transformation which matches these features.‘65 Bolstered by the advancement of computational speed and power, image segmentation has been shown to play an important role in retrospective image registration techniques.

Principal Axes Transformation. In an example of registration as a process in segmentation, Dhawan et al. use the principal axes transformation method to register the brain MR images of a number of subjects to develop a computerized anatomical atlas.‘74 This model is used to automatically segment an arbitrary 3D MR data set. Further, the composite model is then registered to its corresponding PET image via the principal axes transformation, allowing the analysis of selected volumes of interest of metabolic activity.‘74 The principal axes transformation is based in classical theory of rigid bodies and assumes the corresponding bounded volumes to be registered are identified, as, in turn, are their respective centers of mass, inertia matrix, and principal axes. 175In an earlier study, Alpert et al. manually delineated the boundaries of the volumes to be registered before applying the principal axes transformation to MR, PET, and CT data.175 In both studies, the registration process is fed the result of a manual segmentation. This method relies on the objects to be matched to be completely covered by the scans to be registered. If this condition holds, the registration is fairly straightforward and could benefit greatly from automatic segmentation methods. If this condition does not hold, however, the calculation may be invalid.‘65 Holupka and Kooy address this problem by deriving an analytical representation for the surfaces involved, which they claim allows the filling in of missing data.‘76 However, since the analytic expression is interpolating, it may not accurately fill in missing regions, especially if those regions are large or complex.165

Surface Matching Methods. Pelizzari et al. introduced the surface fitting method commonly referred to as the “hat on head”method.i7’ In this method, the authors create 3D models of the surfaces to be registered by outlining contours. These surfaces are most commonly those formed by the exterior and tissue, on serial slices of each scan. By convention, the “head” model comes from the scan covering the larger volume or having the highest resolution if covered volume is comparable.170 A standard minimizing algorithm is applied to residuals of the mean squared distance between the “hat” points and the “head” surface. The early version of the algorithm used a simple binary thresholding method for surface segmentation and due to poor resolution in PET, initially resulted in poor identification of the surfaces.16’ Improved segmentation schemes for PET and SPECT images to segment brains and external surfaces have led to higher registration accuracies.165,‘73 User interaction manually provides a good initial guess at the registration with the aim of avoiding local minima.‘68 At present, the “hat on head” method does not seem to be bounded by the quality of MRI segmentation, but rather on those segmentations of the images from the other mo-

Landmark Matching and Atlas-Based Registration. In landmark matching methods, sets of 3D points from homologous images are first identified then matched spatially.58 The sets of points may be obtained from external fiducial markers177 or internal anatomical landmarks, presently identified by trained experts?* Based on the Procrustes algorithm, the registration method matches equivalent points from these two sets. The Procrustes algorithm finds the least-squared solution by minimizing the r.m.s. distance among all paired

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points.5s*178 Note the difference from the “hat on head” algorithm which minimizes the distance between a set of points and a surface. Neelin et al. segmented MR images to create simulated PET images to validate their MRI/PET landmark registration method.17’ The simulated PET images were created from MRI data which was initially segmented into gray matter, white matter, and CSF. Evans et al. present a method to correlate MRI and PET images in 3D using a volume-of-interest (VOI) atlas.177 The method employed to facilitate the registration (obtain the homologous points) is left to the discretion of the user and the choices include the use of a fiducial head attachment, interactive identification of equivalent points using internal landmarks, or visual registration using a subset of the entire brain atlas.177 Avoiding a problem that plagues data-driven segmentation methods, model- or atlas-based strategies do not depend on MR image quality which has a tendency to be degraded by noise and artifacts from factors such as patient motion and MR field inhomogeneities.1g The Montreal Neurological Institute (MNI) of McGill University has utilized landmark registration methods to develop, among other things, a probabilistic MRI brain atlas (model) that encompasses in a standardized manner the variability of human cortical anatomy and can be used to automatically segment and label 3D MRI datasets by tissue type, specific neuroanatomical volume, and surface parameterization.58 In one study, the authors develop a model which provides a probabilistic mask used to segment MS lesions in MR images of the brain. lg Registration was achieved by transforming a number of healthy volunteer datasets to standardized Talairach space.1g,‘7g Afterwards, a two-step segmentation process was applied to the data consisting of a manual threshold-based edge-tracing function followed by a Bayesian classifier that labeled all the voxels as CSF, white or gray matter, or background. The volunteer data sets were then averaged to create the probability template. This study, like the knowledge-based brain analysis study by Dhawan and Arata,174 uses a segmentation process in the creation of a model which in turn is to be used to segment arbitrary data sets. Therefore, it follows that improvements in segmentation techniques will add to the accuracy of these model-based segmentation techniques. In further studies, Evans et al. use the thin-spline algorithmiEO to deform the VOI atlas to match individual MR image volumes to regionally segment MR images and label specific brain structures. 58 In an innovative approach independent of any prior manual structure identification, Collins et al. developed a multiresolution method to automatically identify and register individual brain regions to a standardized Talairach space, with accuracies comparable to the surface matching and landmark-

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based methods.171 This registration method coupled with the probability template segmentation method fuses into a seemingly automatic segmentation method for an arbitrary dataset. Comparison of Methods. A comparison of both the surface fitting method and the principal axes transformation method on “digital phantoms” was reported by Rusinek et al. 18’ The authors concluded that the surface fitting method is the method of choice for the registration of brain images, mainly due to that method’s relative stability in terms of accuracy. However, they did find a major weakness with both methods in “their sensitivity to local deformations in surface contours.” 18’ The surface fitting procedure compensates for this weakness by allowing the user to inspect the residual distance between the surfaces to detect “problematic cases.” lsl In another report, a comparison between the surface fitting technique and a landmark-based technique was made for the 3D registration of PET and MR images. ls2 Both methods yielded consistent results within the limits imposed by the uncertainty in the homologous point identification in functional and anatomical images. lg2 As such, practical issues concerning the implementation of each method become the important determinants in the choice of method. The surface fitting method has modest requirements for hardware and software,182 but occasionally needs user intervention to “nudge” the algorithm out of a local minimum due to the algorithm’s iterative nature.58,182 The surface fitting procedure may also require manual editing of the images to ensure continuous surfaces and fairly uniform image contrast. 58The disadvantages of the landmarkbased method include the need for sophisticated hardware and software1s2 and the manual identification of anatomical landmarks by a trained expert.58,182 The level and area of expertise needed is thought to be in neuroanatomy and functional image analysislE2 or more simply in pattern recognition.58 An automatic multiresolution method introduced by Collins et al. showed comparable results to both landmark-based and surface fitting methods, and eliminated the requirement these methods impose of manual intervention.“l 5.3. Conclusions The advancement in computing power and speed and graphics capabilities has shifted the focus of registration toward retrospective techniques over prospective techniques, which require trained supervision of all of a patient’s examinations. This supervision is highly impractical, especially in light of the inherent level of error still present.lE3 Of the retrospective techniques, the surface fitting (“hat on head”) and

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landmark-based methods seem comparable to one another, and better than other methods.58*165*‘81 These comparable results obtained through the various method oblige the users to define the practical limitations facing them. However, it may be agreed upon that the automation of accurate segmentation processes would greatly benefit registration as it would free up the need for trained personnel to acquire the data or experts to retrospectively manipulate the data. 6. CONCLUSIONS Image segmentation will clearly be an increasingly integral aspect of most image processing methods applied to MRI data, which reflect both anatomical and functional metrics. The clinical acceptance of these methods will greatly depend on the ease of computation and the reduction of operator dependence on their performance. Advances in high speed computing and high resolution graphics should allow close to real time analysis of images and simultaneous 3D displays of anatomical and functional features. In the same context, the image processing methods will potentially impact the efficiency of diagnosis or therapy protocols, particularly if the methods result in some form of intelligent image fusion of multiple 3D image data sets. Finally, with the current emphasis on cost-effective health care delivery, these methods should impact the ability to perform centralized and remote diagnosis. Acknowledgment-This researchwas supportedby a grant from NC1 (grant # lROlCA59425-01).

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