- Email: [email protected]
Bone feature analysis using image processing techniques
Comput.
Pergamon
Med. Vol. 26, No. 1. pp. 65-76, 19% Copyright @ 1996 Elsevier ScienceLtd Printed in Great Britain. All rights reserved GillO-48251% $15.00+0.00 Biol.
00104825(95)ooo44-5
BONE FEATURE ANALYSIS USING IMAGE PROCESSING TECHNIQUES Z.-Q, LIU,* T. AUSTIN,* C. D. L. THOMAS~ and J. G. CLEMENT?* *Computer Vision and Machine Intelligence (CVMI) Lab., Department of Computer Science. The University of Melbourne, Parkville, VIC 3052, Australia; tSchoo1 of Dental Science, The University of Melbourne, Parkville, VIC 3052, Australia; and $Victorian Institute of Forensic Pathology, 57-83 Kavanagh Street, South Melbourne, VIC 3205, Australia (Received
6 February
1995; received
in revised
form
29 August
1995)
Abstract-In order to establish the correlation between bone structure and age, and information about age-related bone changes, it is necessary to study microstructural features of human bone. Traditionally, in bone biology and forensic science, the analysis if bone cross-sections has been carried out manually. Such a process is known to be slow, inefficient and prone to human error. Consequently, the results obtained so far have been unreliable. In this paper we present a new approach to quantitative analysis of cross-sections of human bones using digital image processing techniques. We demonstrate that such a system is able to extract various bone features consistently and is capable of providing more reliable data and statistics for bones. Consequently, we will be able to correlate features of bone microstructure with age and possibly also with age related bone diseases such as osteoporosis. The development of knowledge-based computer vision systems for automated bone image analysis can now be considered feasible. Bone cross-section features Micro-radiographic bone images Clustering Age determination Image processing
1. INTRODUCTION An important requirement in bone biology is the ability to identify and measure various features of the cross-section of a bone. Such analyses have been used to estimate age at death from skeletal remains, to aid the understanding of bone growth and to assess metabolic or systemic bone disorders such as osteoporosis. In order to obtain useful information from a bone cross-section, bone micro-structures are magnified and measurements of features are taken, Information such as the length, area and frequency of occurrence of certain features are usually recorded. These data are used to calculate the percentage of different structures comprising bone cross-sections. Currently, bone images are observed under a microscope and their analysis follows subjective assessment and manual segmentation. However, it is difficult to make accurate measurements at low magnification and when a cross-section has been sufficiently magnified then the image is too large to be processed manually. In addition, it is commonly acknowledged that, even in well controlled environments, manual processes inevitably introduce subjective biases and they are prone to human errors. As a consequence, analyses that have been derived from very small data sets are likely to produce results that are inconsistent and this inference is supported by the disparity of results in the literature of bone biology. There is therefore a need for developing a method for processing bone images that is both objective and automated. Thanks to rapid developments in computer technology, cost effective computers have become readily available to the medical community and have already found many successful applications including medical image processing. In this paper we present an image processing system for quantitative analysis of bone images. Although humans are readily able to recognize similar and specific features in 65
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bone images, the automated recognition of such regions still represents a considerable challenge to many traditional image processing methods. We will also discuss the problems associated with bone images and present the statistical results obtained from a pilot study of bone samples taken from three widely different age groups. 1.1. Estimating age at death Bone obtained from cadavers is the usual source of material for study. For age estimation, cross-sections taken from the femur, tibia or fibula are usually used. They are cut from the midshaft and a thin ground section of the bone is prepared by abrasive lapping until a plan-parallel section of the desired thickness is obtained. This bone section is then photographed using imaging methods that highlight the bone features deemed appropriate to the particular study. Since the 1930s [l] several methods have been proposed using bone cross-sections to estimate age at death. These methods vary significantly from each other and often use different bone features to provide the evidence from which the eventual conclusions are drawn. Although there is no agreement as to exactly which microstructure features are most important and indicative in determining age, a review of the various methods used provides a good idea of which features or regions need to be delineated and extracted from the bones. In 1960, Jowsey used 24 examples of microradiographic images in a study of bone microstructure [l], and concluded that in sub-adults there was a greater quantity of bone formation and resorption, suggestive of a higher rate of turnover. In older adults there was an increase in the overall rate of resorption. Frost studied 19 primary samples and concluded that with age the rate of individual osteon formation decreased [ 11. This means that the period taken from the resorption space to be filled by an osteon is longer. There is little or no age-related change in the osteon diameter or Haversian canal size. Thompson [2] extracted a total of 28 different measurements and statistics from each of the 166 bone samples used. These variables included the weight, mineralization and density of the bone. Also used were many other variables relating to the number, area and circumference of the osteons and canals. The ratios of the different features were compared. In all these tests, bone cross-sections were processed manually. As a result, the researchers could only make their conclusions based on a small percentage of the bone cross-section area that was available for study. From the statistical point-of-view, these samples used by previous workers must be considered to be too small to make any robust qualitative and quantitative conclusions. This situation is reflected in many conflicting and varying test reports [3] in bone-based age estimation. 1.2. Useful bone features Although there are very few studies using exactly the same structural information for analysis, important bone features are the number, area and circumference of nonHaversian canals (primary osteons), osteons (secondary osteons) and osteon fragments. The changes in the proportions of these constituents in a sense summarize the biological history of the organ. Also of importance is the remaining lamellar bone that is not part of an osteon or osteon fragment. Figure 1 illustrates the important bone features. The image shown here is a microradiograph. Similar features are also visible using other imaging methods. 2. BONE
IMAGES
In this section we will briefly introduce different bone imaging methods and discuss particular features of bone images. Although there are several image modalities available, in this paper we present results only from microradiographic images, as these are probably still the most typical image modality used.
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Fig.1. Useful features of a microradiographic
bone image.
2.1. Obtaining bone images For this particular study, the bone specimens were taken from a reference collection established at the University of Melbourne, School of Dental Science. The material was collected post-mortem at the Victorian Institute for Forensic Pathology (VIFP), Melbourne, Australia, from people who had died suddenly with no known diseases directly affecting their bones. Information on the age, sex, height, weight and, in almost every case, the cause of death was available. Specimens 2-4 cm in length were sawn from the mid-shaft of femurs and fixed in 70% ethanol. Transverse sections, approximately 200 pm thick, were obtained from these using a Leitz 1600 sawing microtome and these sections were air dried and stored flat between paper-lined, glass slides. Hand lapping methods were used to reduce the section thickness to 100 pm + 5 l.un. A three-chip colour video camera (Sony model DXC-930P) was attached to a Leitz Dialux 20 microscope which was fitted with a x 1 magnification objective lens; the field of view with this arrangement was 3.5 mm by 2.5 mm. Iinages of the bone sections were acquired using a video digitizer with a resolution of 512 by 576 pixels (TrueVision Targa + ). The video digitizer was installed in a 33 MHz ‘486 based MS/DOS computer. The imaging software used was Bioscan Optimas V4.02 (Bioscan, Edmonds, Washington, U.S.A.) running under Windows V3.1. The system was calibrated by acquiring and measuring an image of a scale with 0. lmm divisions (Stage micrometer No. 310345. Wild, Heerbrugg, Switzerland). Variations in thickness of the bone slice can cause distortions in images. For instance, a thicker bone region will cause a bone image to appear darker and relatively more mineralized than it actually is. This can easily cause problems in analysis and is minimized by the production of plan-parallel sections of even thickness. There are a number of different methods by which bone samples can be photographed: microradiography, transmitted white light, plane polarized light and circularly polarized light. Each of the imaging techniques brings out different structural features in the bone structure and produces images with significantly different appearances. Figure 2 shows examples for the modalities listed above. The actual bone image sample is photographed using one of the above mentioned imaging methods. The resulting photograph can be viewed using a high precision x-y stage coupled with a microscope, a color video camera, and a computer. The x-y stage is a movable platform situated under the microscope and controlled by stepper motors. It can be controlled by a joystick, as well as by a serial connection To record an image sample, the camera is stepped across the viewing area to grab image frames. An automated process has also been developed so that the entire bone area can be recorded with the exact alignment of the frames. This system records each
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Fig. 2. Examples of the different imaging methods and the types of images they produce for the bone samples.
area in the bone cross-section only once and there is neither overlap nor gap between image frames. For reliable statistics, 50 bone images were obtained from each bone cross-section in the sample. A significant amount of disk space is required for storing data describing a large number of bone samples. 2.2. Structure of bone images As mentioned before, in order to produce quantitative results it is necessary to measure areas, circumferences, and percentages of the total area of the useful features in bone images. The features to be extracted are shown in Fig. 1. In this image the Haversian canals can be easily seen, being almost black. The surrounding osteons can also be fairly easily identified, although some boundaries are not clearly defined. The fragments can only be identified by finding osteon regions that are not surrounding a canal. Previous histological methods developed for determination of age at death use between two and four circular subregions (fields of view) of the bone. Locations of these subregions can be either fixed or random. Most methods use regions near the outer edge of the bone as these areas are more likely to show the effects of aging. However, because of cortical drift (effectively a translation and reformation of the whole bone organ in physical space) features vary quite significantly from area to area. Therefore, it is
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preferable to analyze the entire bone cross-section rather than only small elected and isolated areas. In some studies it has been suggested that the use of only small regions of the bone has the advantage of causing less damage to the bone than removing a complete cross-section [l] and this may be important in museum collections of rare anthropological material. However, in our case, because we have a collection comprising complete cross-sections of bones from over 230 cases, analyzing entire bone cross-sections is feasible. Furthermore, processing such a large sample enables us to obtain more complete and statistically reliable data. 3. METHODS Bone images usually exhibit poor contrast. Furthermore, features in these crosssectional images are often obscured and degraded by artifacts and the imaging process itself. In order to obtain accurate quantitative information and to carry out effective and reliable analysis, it is necessary to apply image preprocessing to remove artifacts and degradations such as blurring and noise. Another significant problem is the difference between various samples of the same object. Deciding which features are of importance and defining how to recognize them reproducibly are challenging problems in automated bone image analysis. Nevertheless, bone images share many common problems which are also present in other types of images. Consequently, many image processing techniques developed over the years can be applied to bone images. In the following sections, we will discuss the techniques used for processing bone images. 3.1. Pre-processing
As already mentioned in previous sections, there are some difficulties with bone images and some pre-processing may be necessary. The aims of such pre-processing are to remove as much noise as possible, to eliminate intensity variations, and to suppress microstructure features that are judged to be unimportant at this time. 3.1.1. Bright-field correction. Because of the large field of view being used, The illumination in the image acquisition system is slightly brighter in the center of the viewing field than at the edges. To rectify this problem, a simple brightness correction is performed. In this method we store an image with a uniform neutral density filter in place of the object. This image is referred to as a bright-field image and it is used as a reference to correct the intensities of actual bone images. Each image is divided pixel-wise by the bright-field image to correct light variation. Because the optical density in microradiographs of bone is a function of bone mineralization, correcting the brightness ensures that the mineralization levels of the bone are correctly represented.
3.1.2. Adaptive neighborhood processing. Bone images often have a significant amount of noise which obscures features, making both feature extraction and analysis difficult. There are a large number of noise reduction algorithms available in the literature [4], which fall into the category of deterministic or stochastic approaches. However, most such techniques are based on global assumptions about the nature of the noise that do not reflect real world situations, particularly in the imaging process. As a result, these methods are, in principle, globally smoothing filters. While these filters are able to remove noise, they also blur (thereby degrading) the resulting image. This renders the further analysis of the preprocessed image even more difficult. Liu et al. [5] have pointed out that such techniques are in fact non-robust and they proposed that in order to suppress noise without degrading image features it is necessary to develop filters that are adaptive and based on both global and local image properties. Using this approach, Paranjape et al recently [6] developed an adaptive neighborhood approach to image histogram equalization and noise reduction, and have applied it to
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medical images with satisfactory results. In their method they used variable window sizes depending on the region of the image being processed. The adaptive neighborhood is a compound region made up of a foreground that contains &connected pixels and a background of neighboring pixels moulded around the foreground. Their method is able to move noise without blurring and block-edge artifacts. Due to its demonstrable advantages over some better-known techniques, in this project we use this method for bone image enhancement. Primarily, the adaptive neighborhood (AN) method consists of the following two steps: l l
determination of the neighborhoods, noise reduction using the neighborhood
information,
In the AN process, the neighborhood of a pixel is calculated by first starting with a so-called seed pixel. The neighborhood of the seed pixel consists of g-connected pixels satisfying an additive or derivative tolerance property compared to a predetermined threshold. The additive condition is given by. kkYhG7~)l-L whereas the derivative
(1)
condition: (2)
where g(i, j) is the seed pixel and g(x, y) is an image pixel in the neighborhood, and the thresholds Z”l and T2 are determined experimentally. The condition used is dependent on the type of noise present in the image. For a complete flow-chart description of the algorithm please refer to [6]. However, for images with graduated change or no apparent edges, region determination may be difficult. AN is able to obtain excellent results for images with relatively strong regional areas. Since microradiographic bone images generally have reasonably good regional characteristics, AN is most suitable for such applications. Once the regions have been determined, the actual smoothing must be performed. The smoothing is carried out by using the pixels within a window of fixed size. Conventionally, the mean (average) value of these pixels is used to place the pixel at the center of the window. In general, for a window of IZ x n, the averaging operation for the new pixel is given by: (3) The smoothing operation using adaptive neighborhoods more traditional counterpart: n n
is a slight modification
of its
(4) where nh(x, y) is the neighborhood, g(i, j) is the original image and g’(x, y) the smoothed image. The value g’(x, y) is then normalized depending on the number of pixels present in the window. Bone images have high frequency noise due to the imaging process, and also contain some sharp boundaries at the edges of Haversian canals. A traditional smoothing operation, with a fixed n x n averaging window, will reduce the noise, and also blur these sharp boundaries. However, adaptive neighborhood smoothing is able to reduce noise while maintaining sharp boundaries. Adaptive neighborhood smoothing is similar to some other noise reduction methods, such as sigma-nearest neighbors mean smoothing [4]. Adaptive neighborhood smoothing has the advantage of being based on &connected pixels, rather than being just concerned with single pixel intensity. This makes it possible to merge small regions into larger
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Fig. 3. (a) Original image. (b) Image smoothed with an n x 12tixed neighborhood. (c) Image smoothed with a rotating mask. (d) Image smoothed with adaptive neighborhood smoothing.
regions. Therefore, very small areas of high noise are also subject to smoothing, whereas single pixel neighbor determination would make them into a separate region. Merging small regions into larger surrounding areas is very effective in reducing the presence of unwanted surface texture in microradiographic bone images. High-level noise will also affect bone image segmentation. It is therefore necessary to reduce noise and some variations in surface texture in order to obtain better results. To show the effectiveness of the adaptive neighborhood algorithm, we have also processed images with a rotating window smoothing algorithm [4]. This algorithm uses a fixed n xn window that always includes the pixel to be replaced, but it not always centered on that pixel. The most homogenous region surrounding the pixel is used for smoothing. This avoids blurring edges by ensuring that smoothing is performed only on one side of an edge. At pixel g(i, j), within a large region R (centered on the current pixel g(i, j)), it pixels that form the window (mask) are used. The mask with the lowest o* is chosen, where o* is calculated by:
For comparison Fig. 3 shows the smoothing results using hxed window, rotating mask, and adaptive neighborhood algorithms, respectively. The result produced by using the rotating mask methods (Fig. 3(c)) is pleasing to the eye, but introduces some noise into the image. This noise emphasizes small, strong variations and therefore some of the bone
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surface textures that are not useful in the analysis have been amplified. If some noisy areas in the image are of similar size to those of the mask, this area will be spread out rather than being reduced. The best result for noise suppression in microradiographic images is achieved by using adaptive neighborhood smoothing (eg. Fig. 3(d)). 3.2. Image segmentation
After the input image has been pre-processed, the image has to be segmented according to the desired features discussed in the previous sections, namely, primary osteons, secondary osteons and osteon fragments. Clustering is an unsupervized learning technique which, based on a similarity measure, labels data with different group (cluster) identities. In the resultant data set, those belonging to a particular cluster share significant similarity in some aspects when compared with the data in other clusters. Clustering has been used for dealing with many pattern or region finding problems, in this application, we propose to use clustering for image segmentation. Our experiments have shown that such an approach produces satisfactory results for the present task. Although there are many clustering algorithms available in the literature [7], we have chosen the well-known K-means clustering algorithm for its simplicity and effectiveness. The specific clustering program used is called CLUSTER [7,8], which performs K-means clustering. This algorithm requires the user to specify the number (k) of clusters in the data. The input image to be clustered (segmented) is represented as a two dimensional feature vector. CLUSTER uses a simple squared-error cost function to assign each pixel in the input image to one of the k clusters being generated. When this cost function is minimized, the pixel is assigned to the closest cluster. 3.3. Application
to images
The input is a microradiographic bone image. The number of clusters can be easily determined by the number of features required for analysis. In this application, we chose the number of clusters k = 5, as this corresponds to the features in bone cross-section. That is, each type of region in the image corresponds to an important microstructural feature. However, for each type of region there is also a number of unconnected individual regions. This is to be expected as there is more than one osteon or canal in the bone image. Figure 4 shows segmentation (clustering) results for some of the bone images tested. We will discuss the results in the next section. 4. EXPERIMENTS To process the microradiographic bone images, the system has been implemented in C/UNIX. The routines to determine neighborhoods, reduce noise, label regions and to produce statistics have been coded. Clustering, covariance (for transmitted white light images), and relabeling routines have also been developed. Using this system, we have processed a large number of bone images and will discuss the results in this section. The detailed quantitative (statistical) results have been obtained for microradiographic images, though some transmitted light bone images have also been processed. The following modules are used to process and segment the images. Bright-field correction. Adaptive neighborhood smoothing. 0 Clustering into regions l Relabeling of unconnected regions. l Analysis of Image, production of statistics.
l
l
From the large database of bone samples processed, six were selected for testing. We selected these samples to represent males and females of three age groups-20,40 and 60 years. Detailed information about each sample is shown in Table% Figure 4 shows
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Fig. 4. (a) and(b) Images from sample 5, (c)and (d) images from sample 194, (e) and (f) images from sample murg.
some of the original and processed image samples. We used CLUSTER to segment the bone image according the five features; the Haversian canal, dark osteon, osteon. light osteon and lamellar bone. The initial segmented images contained only five different intensity values. For each of the values there were a number of regions corresponding to the features of the same type in the image. In bone images, there are some isolated osteons, whereas lamellar areas are often completely connected. The general information extracted, such as the percentage of each feature in the bone cross section could be obtained directly from these types of images. However, for such quantitative information as the number of osteons and the Table 1. Details of the specific bone samples used Sample number 21 25 75 194 5 62
Age
Sex
Area
Perimeter
Width
19 20 40 41 59 60
M F M F M F
414 436 49% 339 525 364
85.63 88.26 99.84 17.19 108.9 89.43
6.28 6.40 6.19 5.73 5.79 4.92
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Table 2. Statistics calculated for the images: each value indicates the ratio between the area of each feature and the total area of the image Bone image 194edge 2lcent 21wi 21wo 25cent 25edge Scent Sedge 62wi 62~0 murgl murg2 murg3 Averages
4%
Canal
Lamellar
Dk ost.
Osteon
Lt est.
Total ost.
41 19 19 19 20 20 59 59 60 60 20 50 60
0.065 0.041 0.038 0.025 0.044 0.060 0.324 0.168 0.059 0.073 0.028 0.058 0.084 0.082
0.290 0.041 0.168 0.387 0.194 0.172 0.380 0.560 0.635 0.581 0.471 0.171 0.153 0.310
0.091 0.188 0.180 0.291 0.122 0.223 0.089 0.087 0.103 0.134 0.192 0.126 0.364 0.167
0.242 0.385 0.386 0.264 0.295 0.274 0.185 0.154 0.177 0.186 0.288 0.402 0.390 0.282
0.285 0.249 0.212 0 0.319 0.247 0 0 0
0.618 0.822 0.778 0.555 0.736 0.744 0.274 0.241 0.280 0.320 0.480 0.751 0.754 0.579
8 0.223 0 0.130
measurement of individual osteons, the unconnected regions have to be determined. This is done by a relabeling procedure which relabels individual regions. The segmented images are analyzed to produce statistics about the composition of each image. Table 2 shows the values for the six major features-Haversian canals (Canal), lamellar (Lamellar), dark osteons (Dk ost.), osteons (Osteon), light osteons (Lt osteons) and total osteons (Total ost.) which is the sum of dark osteons, osteons and light osteons (columns 5-7). Each feature is given as the ratio between the area of the feature and the total area of the image, where areas are measured in numbers of pixels. We can see clearly from Table 2 that bone loss due to the aging process results in reduced area of osteons, for instance, at age 19 (2lcent, 21wi and 21~0 in the table), the total osteon area can be as high as 0.82. However, at age 60 (62wi and 62~0) the total osteon area drops to about 0.30. As discussed previously, our goals of image processing are to identify and segment bone images into regions, and to produce quantitative information about these regions. Figure 4 shows results for some of the bone images tested. It can be seen that the image processing system here described has successfully segmented the bone images. Non-Haversian canals, osteons, and lamellar bone are clearly separated and are segmented as distinct regions. Due to osteonal variation, they are separated into three types, depending on whether the osteon is more mineralized (lighter) or less mineralized (darker). It is useful to distinguish between these cases, as (on a microradiograph) darker osteons often indicate that they have been formed in the bone more recently than lighter, more heavily mineralized osteons. However, there remains a small error in region classification in some of the results. This is probably due to the intensity variation around the edge of the canals and the consequent difficulties in defining the precise boundary. This can be merged with the surrounding osteons to improve accuracy further. 5. CONCLUSIONS In this paper we have discussed the traditional approaches used for bone analysis and the problems associated with their use. In order to automate the analysis process, we have developed an image processing system for rapid quantitative bone image analysis. Such a system is desirable in that it can provide more consistent and reliable results than have been obtained previously and is also capable of examining the whole of the crosssection of a bone. With this system, it is now possible to process a large number of bone images. This will enable further studies to investigate any possible correlation between chronological age of the person and the bone microstructure to be undertaken. These studies will be able to avoid many of the measurement problems found with previous manual methods. It is envisaged that such a system will also be very useful for studies of osteoporosis.
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We are currently investigating knowledge-based techniques to combine features from different bone image modalities, i.e. transmitted white light, microradiographs, plane polarized light, circularly polarized light, to utilize fully the information available for better bone image analysis. For instance, while some important features can be obtained from microradiographic images, differentiating between osteons and lamellar areas is difficult. Processing transmitted light images would be particularly useful in conjunction with corresponding microradiographic images as these images contain textural information. Such images are rather difficult to process by conventional image processing techniques including some of the newly developed algorithms in the literature. Nevertheless, we have recently developed a simple covariance methods for processing of texture [9]. Initial tests have shown that our covariance method was able to distinguish between the Haversian canals and o&eons with high accuracy. Despite the irregular appearance of bone images, there is still a great deal of order to the image. For instance, osteons are usually centred on a canal and are proportional in size to each other. Fragments of osteons are derived from previous generations that have been partially removed and replaced by new osteons. These are other features in bone images are fairly constant in their expression across divergent examples of bones and reflect underlying fundamental biological constants. In view of this, it would seem to be desirable to utilize the features inherent in the bone structure or its segmentation. Of particular interest is the extraction of circular or oval shapes from bone images. Although there is only a limited number of useful features that are present in the image, the appearances of these features can vary significantly from sample to sample. We envisage that methods will soon be developed in the near future that make appropriate use of the available knowledge of bone structures and will thereby have improved performance and practicality over existing methods of bone feature analysis. would like to acknowledge Professor AIan Boyde for providing Fig. 2(c) and 2(d). This research is supported in part by a grant from the Australian Research Council.
Acknowledgements-We
REFERENCES 1. M. R. Zimmerman and J. L. Angel (Eds), Dating and Age Determination of Biology Materials. Croom Helm (1986) 2. D. Thompson, The core technique in the determination of age at death in skeletons, J. Forensic Sci. 902914, (1979). 3. M. Y. Iscan and S. R. Loth, Osteological manifestations of age in the adult. In Reconsrruction ofLife From the Skeleton, Alan R. Liss, M. Y. Iscan and K. A. R. Kennedy (Eds), New York (1989). 4. M. J. Wang, W. Y. Wu and C. M. Liu, Performance evaluation of some noise reduction methods, Computer Vision, Graphics and Image Processing, 54, 134-146 (1992). 5. Z. Q. Liu and T. M. Caelli, A sequential recursive filter for image restoration Computer Vision, Graphics, and Image Processing 44,296306 (1988). 6. R. Paranjape, W. Morrow and R. Rangayyan, Adaptive-neighborhood histogram equalization for image enhancement, Computer Vision, Graphics and image Processing 54, 296-306 (1992) 7. R. C. Dubes and A. K. Jain, User’s dilemma, Pattern Recognition 8, 247-260 (1976). 8. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, New Jersey (1988). 9. S. Madiraju and Z. Q. Liu, On rotation invariant texture processing techniques, Proc. The 1st IEEE Inf. Conf Image Processing, ICIP’94, Vol.11, 655-659, Austin, Texas, November 13-16, (1994). About the Author-ZHI-QIANG LIU is currently a senior lecturer with the Department of Computer Science, The University of Melbourne. He received a M.A.Sc. degree in Aerospace Engineering from the Institute for Aerospace Studies, The University of Toronto, and a Ph.D. degree in Electrical Engineering from The University of Alberta, Canada. He is the leader and the Principal Investigator of the Computer Vision and Machine Intelligence Laboratory (CVMIL) at the Dept. of Computer Science, The University of Melbourne. His research interests include image processing, computer vision, neural networks and intelligent and knowledge-based systems. About the Author--TIM AUSTIN received a BE (Hons) degree in Electrical Engineering, a BSc degree in Computer Science, and Master of Engineering Science, all from the University of Melbourne. Currently, he is an engineer at Telstra Australia. About the Author-DAVID THOMAS is a Professional Officer with the University of Melbourne, School of Dental Science. His research interests include the characterisation and testing of bone
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Z.-Q. Lru et al. (with application to forensic analysis and to osteoporosis), the computer modelling of teeth and the analysis of the human face. About the Author-JOHN CLEMENT is an Associate Professor in Oral Anatomy and Forensic Odontology, at the University of Melbourne. He is also an Honorary Associate Professor (Clinical) with Department of Forensic Medicine and an Honorary Research Associate with Department of Physics at the Monash University. He is a consultant forensic odontologist in charge to the Victorian Institute of Forensic Medicine and a Victoria Police Forensic Dentist.
Pergamon
Med. Vol. 26, No. 1. pp. 65-76, 19% Copyright @ 1996 Elsevier ScienceLtd Printed in Great Britain. All rights reserved GillO-48251% $15.00+0.00 Biol.
00104825(95)ooo44-5
BONE FEATURE ANALYSIS USING IMAGE PROCESSING TECHNIQUES Z.-Q, LIU,* T. AUSTIN,* C. D. L. THOMAS~ and J. G. CLEMENT?* *Computer Vision and Machine Intelligence (CVMI) Lab., Department of Computer Science. The University of Melbourne, Parkville, VIC 3052, Australia; tSchoo1 of Dental Science, The University of Melbourne, Parkville, VIC 3052, Australia; and $Victorian Institute of Forensic Pathology, 57-83 Kavanagh Street, South Melbourne, VIC 3205, Australia (Received
6 February
1995; received
in revised
form
29 August
1995)
Abstract-In order to establish the correlation between bone structure and age, and information about age-related bone changes, it is necessary to study microstructural features of human bone. Traditionally, in bone biology and forensic science, the analysis if bone cross-sections has been carried out manually. Such a process is known to be slow, inefficient and prone to human error. Consequently, the results obtained so far have been unreliable. In this paper we present a new approach to quantitative analysis of cross-sections of human bones using digital image processing techniques. We demonstrate that such a system is able to extract various bone features consistently and is capable of providing more reliable data and statistics for bones. Consequently, we will be able to correlate features of bone microstructure with age and possibly also with age related bone diseases such as osteoporosis. The development of knowledge-based computer vision systems for automated bone image analysis can now be considered feasible. Bone cross-section features Micro-radiographic bone images Clustering Age determination Image processing
1. INTRODUCTION An important requirement in bone biology is the ability to identify and measure various features of the cross-section of a bone. Such analyses have been used to estimate age at death from skeletal remains, to aid the understanding of bone growth and to assess metabolic or systemic bone disorders such as osteoporosis. In order to obtain useful information from a bone cross-section, bone micro-structures are magnified and measurements of features are taken, Information such as the length, area and frequency of occurrence of certain features are usually recorded. These data are used to calculate the percentage of different structures comprising bone cross-sections. Currently, bone images are observed under a microscope and their analysis follows subjective assessment and manual segmentation. However, it is difficult to make accurate measurements at low magnification and when a cross-section has been sufficiently magnified then the image is too large to be processed manually. In addition, it is commonly acknowledged that, even in well controlled environments, manual processes inevitably introduce subjective biases and they are prone to human errors. As a consequence, analyses that have been derived from very small data sets are likely to produce results that are inconsistent and this inference is supported by the disparity of results in the literature of bone biology. There is therefore a need for developing a method for processing bone images that is both objective and automated. Thanks to rapid developments in computer technology, cost effective computers have become readily available to the medical community and have already found many successful applications including medical image processing. In this paper we present an image processing system for quantitative analysis of bone images. Although humans are readily able to recognize similar and specific features in 65
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bone images, the automated recognition of such regions still represents a considerable challenge to many traditional image processing methods. We will also discuss the problems associated with bone images and present the statistical results obtained from a pilot study of bone samples taken from three widely different age groups. 1.1. Estimating age at death Bone obtained from cadavers is the usual source of material for study. For age estimation, cross-sections taken from the femur, tibia or fibula are usually used. They are cut from the midshaft and a thin ground section of the bone is prepared by abrasive lapping until a plan-parallel section of the desired thickness is obtained. This bone section is then photographed using imaging methods that highlight the bone features deemed appropriate to the particular study. Since the 1930s [l] several methods have been proposed using bone cross-sections to estimate age at death. These methods vary significantly from each other and often use different bone features to provide the evidence from which the eventual conclusions are drawn. Although there is no agreement as to exactly which microstructure features are most important and indicative in determining age, a review of the various methods used provides a good idea of which features or regions need to be delineated and extracted from the bones. In 1960, Jowsey used 24 examples of microradiographic images in a study of bone microstructure [l], and concluded that in sub-adults there was a greater quantity of bone formation and resorption, suggestive of a higher rate of turnover. In older adults there was an increase in the overall rate of resorption. Frost studied 19 primary samples and concluded that with age the rate of individual osteon formation decreased [ 11. This means that the period taken from the resorption space to be filled by an osteon is longer. There is little or no age-related change in the osteon diameter or Haversian canal size. Thompson [2] extracted a total of 28 different measurements and statistics from each of the 166 bone samples used. These variables included the weight, mineralization and density of the bone. Also used were many other variables relating to the number, area and circumference of the osteons and canals. The ratios of the different features were compared. In all these tests, bone cross-sections were processed manually. As a result, the researchers could only make their conclusions based on a small percentage of the bone cross-section area that was available for study. From the statistical point-of-view, these samples used by previous workers must be considered to be too small to make any robust qualitative and quantitative conclusions. This situation is reflected in many conflicting and varying test reports [3] in bone-based age estimation. 1.2. Useful bone features Although there are very few studies using exactly the same structural information for analysis, important bone features are the number, area and circumference of nonHaversian canals (primary osteons), osteons (secondary osteons) and osteon fragments. The changes in the proportions of these constituents in a sense summarize the biological history of the organ. Also of importance is the remaining lamellar bone that is not part of an osteon or osteon fragment. Figure 1 illustrates the important bone features. The image shown here is a microradiograph. Similar features are also visible using other imaging methods. 2. BONE
IMAGES
In this section we will briefly introduce different bone imaging methods and discuss particular features of bone images. Although there are several image modalities available, in this paper we present results only from microradiographic images, as these are probably still the most typical image modality used.
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Fig.1. Useful features of a microradiographic
bone image.
2.1. Obtaining bone images For this particular study, the bone specimens were taken from a reference collection established at the University of Melbourne, School of Dental Science. The material was collected post-mortem at the Victorian Institute for Forensic Pathology (VIFP), Melbourne, Australia, from people who had died suddenly with no known diseases directly affecting their bones. Information on the age, sex, height, weight and, in almost every case, the cause of death was available. Specimens 2-4 cm in length were sawn from the mid-shaft of femurs and fixed in 70% ethanol. Transverse sections, approximately 200 pm thick, were obtained from these using a Leitz 1600 sawing microtome and these sections were air dried and stored flat between paper-lined, glass slides. Hand lapping methods were used to reduce the section thickness to 100 pm + 5 l.un. A three-chip colour video camera (Sony model DXC-930P) was attached to a Leitz Dialux 20 microscope which was fitted with a x 1 magnification objective lens; the field of view with this arrangement was 3.5 mm by 2.5 mm. Iinages of the bone sections were acquired using a video digitizer with a resolution of 512 by 576 pixels (TrueVision Targa + ). The video digitizer was installed in a 33 MHz ‘486 based MS/DOS computer. The imaging software used was Bioscan Optimas V4.02 (Bioscan, Edmonds, Washington, U.S.A.) running under Windows V3.1. The system was calibrated by acquiring and measuring an image of a scale with 0. lmm divisions (Stage micrometer No. 310345. Wild, Heerbrugg, Switzerland). Variations in thickness of the bone slice can cause distortions in images. For instance, a thicker bone region will cause a bone image to appear darker and relatively more mineralized than it actually is. This can easily cause problems in analysis and is minimized by the production of plan-parallel sections of even thickness. There are a number of different methods by which bone samples can be photographed: microradiography, transmitted white light, plane polarized light and circularly polarized light. Each of the imaging techniques brings out different structural features in the bone structure and produces images with significantly different appearances. Figure 2 shows examples for the modalities listed above. The actual bone image sample is photographed using one of the above mentioned imaging methods. The resulting photograph can be viewed using a high precision x-y stage coupled with a microscope, a color video camera, and a computer. The x-y stage is a movable platform situated under the microscope and controlled by stepper motors. It can be controlled by a joystick, as well as by a serial connection To record an image sample, the camera is stepped across the viewing area to grab image frames. An automated process has also been developed so that the entire bone area can be recorded with the exact alignment of the frames. This system records each
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Fig. 2. Examples of the different imaging methods and the types of images they produce for the bone samples.
area in the bone cross-section only once and there is neither overlap nor gap between image frames. For reliable statistics, 50 bone images were obtained from each bone cross-section in the sample. A significant amount of disk space is required for storing data describing a large number of bone samples. 2.2. Structure of bone images As mentioned before, in order to produce quantitative results it is necessary to measure areas, circumferences, and percentages of the total area of the useful features in bone images. The features to be extracted are shown in Fig. 1. In this image the Haversian canals can be easily seen, being almost black. The surrounding osteons can also be fairly easily identified, although some boundaries are not clearly defined. The fragments can only be identified by finding osteon regions that are not surrounding a canal. Previous histological methods developed for determination of age at death use between two and four circular subregions (fields of view) of the bone. Locations of these subregions can be either fixed or random. Most methods use regions near the outer edge of the bone as these areas are more likely to show the effects of aging. However, because of cortical drift (effectively a translation and reformation of the whole bone organ in physical space) features vary quite significantly from area to area. Therefore, it is
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preferable to analyze the entire bone cross-section rather than only small elected and isolated areas. In some studies it has been suggested that the use of only small regions of the bone has the advantage of causing less damage to the bone than removing a complete cross-section [l] and this may be important in museum collections of rare anthropological material. However, in our case, because we have a collection comprising complete cross-sections of bones from over 230 cases, analyzing entire bone cross-sections is feasible. Furthermore, processing such a large sample enables us to obtain more complete and statistically reliable data. 3. METHODS Bone images usually exhibit poor contrast. Furthermore, features in these crosssectional images are often obscured and degraded by artifacts and the imaging process itself. In order to obtain accurate quantitative information and to carry out effective and reliable analysis, it is necessary to apply image preprocessing to remove artifacts and degradations such as blurring and noise. Another significant problem is the difference between various samples of the same object. Deciding which features are of importance and defining how to recognize them reproducibly are challenging problems in automated bone image analysis. Nevertheless, bone images share many common problems which are also present in other types of images. Consequently, many image processing techniques developed over the years can be applied to bone images. In the following sections, we will discuss the techniques used for processing bone images. 3.1. Pre-processing
As already mentioned in previous sections, there are some difficulties with bone images and some pre-processing may be necessary. The aims of such pre-processing are to remove as much noise as possible, to eliminate intensity variations, and to suppress microstructure features that are judged to be unimportant at this time. 3.1.1. Bright-field correction. Because of the large field of view being used, The illumination in the image acquisition system is slightly brighter in the center of the viewing field than at the edges. To rectify this problem, a simple brightness correction is performed. In this method we store an image with a uniform neutral density filter in place of the object. This image is referred to as a bright-field image and it is used as a reference to correct the intensities of actual bone images. Each image is divided pixel-wise by the bright-field image to correct light variation. Because the optical density in microradiographs of bone is a function of bone mineralization, correcting the brightness ensures that the mineralization levels of the bone are correctly represented.
3.1.2. Adaptive neighborhood processing. Bone images often have a significant amount of noise which obscures features, making both feature extraction and analysis difficult. There are a large number of noise reduction algorithms available in the literature [4], which fall into the category of deterministic or stochastic approaches. However, most such techniques are based on global assumptions about the nature of the noise that do not reflect real world situations, particularly in the imaging process. As a result, these methods are, in principle, globally smoothing filters. While these filters are able to remove noise, they also blur (thereby degrading) the resulting image. This renders the further analysis of the preprocessed image even more difficult. Liu et al. [5] have pointed out that such techniques are in fact non-robust and they proposed that in order to suppress noise without degrading image features it is necessary to develop filters that are adaptive and based on both global and local image properties. Using this approach, Paranjape et al recently [6] developed an adaptive neighborhood approach to image histogram equalization and noise reduction, and have applied it to
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medical images with satisfactory results. In their method they used variable window sizes depending on the region of the image being processed. The adaptive neighborhood is a compound region made up of a foreground that contains &connected pixels and a background of neighboring pixels moulded around the foreground. Their method is able to move noise without blurring and block-edge artifacts. Due to its demonstrable advantages over some better-known techniques, in this project we use this method for bone image enhancement. Primarily, the adaptive neighborhood (AN) method consists of the following two steps: l l
determination of the neighborhoods, noise reduction using the neighborhood
information,
In the AN process, the neighborhood of a pixel is calculated by first starting with a so-called seed pixel. The neighborhood of the seed pixel consists of g-connected pixels satisfying an additive or derivative tolerance property compared to a predetermined threshold. The additive condition is given by. kkYhG7~)l-L whereas the derivative
(1)
condition: (2)
where g(i, j) is the seed pixel and g(x, y) is an image pixel in the neighborhood, and the thresholds Z”l and T2 are determined experimentally. The condition used is dependent on the type of noise present in the image. For a complete flow-chart description of the algorithm please refer to [6]. However, for images with graduated change or no apparent edges, region determination may be difficult. AN is able to obtain excellent results for images with relatively strong regional areas. Since microradiographic bone images generally have reasonably good regional characteristics, AN is most suitable for such applications. Once the regions have been determined, the actual smoothing must be performed. The smoothing is carried out by using the pixels within a window of fixed size. Conventionally, the mean (average) value of these pixels is used to place the pixel at the center of the window. In general, for a window of IZ x n, the averaging operation for the new pixel is given by: (3) The smoothing operation using adaptive neighborhoods more traditional counterpart: n n
is a slight modification
of its
(4) where nh(x, y) is the neighborhood, g(i, j) is the original image and g’(x, y) the smoothed image. The value g’(x, y) is then normalized depending on the number of pixels present in the window. Bone images have high frequency noise due to the imaging process, and also contain some sharp boundaries at the edges of Haversian canals. A traditional smoothing operation, with a fixed n x n averaging window, will reduce the noise, and also blur these sharp boundaries. However, adaptive neighborhood smoothing is able to reduce noise while maintaining sharp boundaries. Adaptive neighborhood smoothing is similar to some other noise reduction methods, such as sigma-nearest neighbors mean smoothing [4]. Adaptive neighborhood smoothing has the advantage of being based on &connected pixels, rather than being just concerned with single pixel intensity. This makes it possible to merge small regions into larger
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Fig. 3. (a) Original image. (b) Image smoothed with an n x 12tixed neighborhood. (c) Image smoothed with a rotating mask. (d) Image smoothed with adaptive neighborhood smoothing.
regions. Therefore, very small areas of high noise are also subject to smoothing, whereas single pixel neighbor determination would make them into a separate region. Merging small regions into larger surrounding areas is very effective in reducing the presence of unwanted surface texture in microradiographic bone images. High-level noise will also affect bone image segmentation. It is therefore necessary to reduce noise and some variations in surface texture in order to obtain better results. To show the effectiveness of the adaptive neighborhood algorithm, we have also processed images with a rotating window smoothing algorithm [4]. This algorithm uses a fixed n xn window that always includes the pixel to be replaced, but it not always centered on that pixel. The most homogenous region surrounding the pixel is used for smoothing. This avoids blurring edges by ensuring that smoothing is performed only on one side of an edge. At pixel g(i, j), within a large region R (centered on the current pixel g(i, j)), it pixels that form the window (mask) are used. The mask with the lowest o* is chosen, where o* is calculated by:
For comparison Fig. 3 shows the smoothing results using hxed window, rotating mask, and adaptive neighborhood algorithms, respectively. The result produced by using the rotating mask methods (Fig. 3(c)) is pleasing to the eye, but introduces some noise into the image. This noise emphasizes small, strong variations and therefore some of the bone
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surface textures that are not useful in the analysis have been amplified. If some noisy areas in the image are of similar size to those of the mask, this area will be spread out rather than being reduced. The best result for noise suppression in microradiographic images is achieved by using adaptive neighborhood smoothing (eg. Fig. 3(d)). 3.2. Image segmentation
After the input image has been pre-processed, the image has to be segmented according to the desired features discussed in the previous sections, namely, primary osteons, secondary osteons and osteon fragments. Clustering is an unsupervized learning technique which, based on a similarity measure, labels data with different group (cluster) identities. In the resultant data set, those belonging to a particular cluster share significant similarity in some aspects when compared with the data in other clusters. Clustering has been used for dealing with many pattern or region finding problems, in this application, we propose to use clustering for image segmentation. Our experiments have shown that such an approach produces satisfactory results for the present task. Although there are many clustering algorithms available in the literature [7], we have chosen the well-known K-means clustering algorithm for its simplicity and effectiveness. The specific clustering program used is called CLUSTER [7,8], which performs K-means clustering. This algorithm requires the user to specify the number (k) of clusters in the data. The input image to be clustered (segmented) is represented as a two dimensional feature vector. CLUSTER uses a simple squared-error cost function to assign each pixel in the input image to one of the k clusters being generated. When this cost function is minimized, the pixel is assigned to the closest cluster. 3.3. Application
to images
The input is a microradiographic bone image. The number of clusters can be easily determined by the number of features required for analysis. In this application, we chose the number of clusters k = 5, as this corresponds to the features in bone cross-section. That is, each type of region in the image corresponds to an important microstructural feature. However, for each type of region there is also a number of unconnected individual regions. This is to be expected as there is more than one osteon or canal in the bone image. Figure 4 shows segmentation (clustering) results for some of the bone images tested. We will discuss the results in the next section. 4. EXPERIMENTS To process the microradiographic bone images, the system has been implemented in C/UNIX. The routines to determine neighborhoods, reduce noise, label regions and to produce statistics have been coded. Clustering, covariance (for transmitted white light images), and relabeling routines have also been developed. Using this system, we have processed a large number of bone images and will discuss the results in this section. The detailed quantitative (statistical) results have been obtained for microradiographic images, though some transmitted light bone images have also been processed. The following modules are used to process and segment the images. Bright-field correction. Adaptive neighborhood smoothing. 0 Clustering into regions l Relabeling of unconnected regions. l Analysis of Image, production of statistics.
l
l
From the large database of bone samples processed, six were selected for testing. We selected these samples to represent males and females of three age groups-20,40 and 60 years. Detailed information about each sample is shown in Table% Figure 4 shows
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Fig. 4. (a) and(b) Images from sample 5, (c)and (d) images from sample 194, (e) and (f) images from sample murg.
some of the original and processed image samples. We used CLUSTER to segment the bone image according the five features; the Haversian canal, dark osteon, osteon. light osteon and lamellar bone. The initial segmented images contained only five different intensity values. For each of the values there were a number of regions corresponding to the features of the same type in the image. In bone images, there are some isolated osteons, whereas lamellar areas are often completely connected. The general information extracted, such as the percentage of each feature in the bone cross section could be obtained directly from these types of images. However, for such quantitative information as the number of osteons and the Table 1. Details of the specific bone samples used Sample number 21 25 75 194 5 62
Age
Sex
Area
Perimeter
Width
19 20 40 41 59 60
M F M F M F
414 436 49% 339 525 364
85.63 88.26 99.84 17.19 108.9 89.43
6.28 6.40 6.19 5.73 5.79 4.92
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Table 2. Statistics calculated for the images: each value indicates the ratio between the area of each feature and the total area of the image Bone image 194edge 2lcent 21wi 21wo 25cent 25edge Scent Sedge 62wi 62~0 murgl murg2 murg3 Averages
4%
Canal
Lamellar
Dk ost.
Osteon
Lt est.
Total ost.
41 19 19 19 20 20 59 59 60 60 20 50 60
0.065 0.041 0.038 0.025 0.044 0.060 0.324 0.168 0.059 0.073 0.028 0.058 0.084 0.082
0.290 0.041 0.168 0.387 0.194 0.172 0.380 0.560 0.635 0.581 0.471 0.171 0.153 0.310
0.091 0.188 0.180 0.291 0.122 0.223 0.089 0.087 0.103 0.134 0.192 0.126 0.364 0.167
0.242 0.385 0.386 0.264 0.295 0.274 0.185 0.154 0.177 0.186 0.288 0.402 0.390 0.282
0.285 0.249 0.212 0 0.319 0.247 0 0 0
0.618 0.822 0.778 0.555 0.736 0.744 0.274 0.241 0.280 0.320 0.480 0.751 0.754 0.579
8 0.223 0 0.130
measurement of individual osteons, the unconnected regions have to be determined. This is done by a relabeling procedure which relabels individual regions. The segmented images are analyzed to produce statistics about the composition of each image. Table 2 shows the values for the six major features-Haversian canals (Canal), lamellar (Lamellar), dark osteons (Dk ost.), osteons (Osteon), light osteons (Lt osteons) and total osteons (Total ost.) which is the sum of dark osteons, osteons and light osteons (columns 5-7). Each feature is given as the ratio between the area of the feature and the total area of the image, where areas are measured in numbers of pixels. We can see clearly from Table 2 that bone loss due to the aging process results in reduced area of osteons, for instance, at age 19 (2lcent, 21wi and 21~0 in the table), the total osteon area can be as high as 0.82. However, at age 60 (62wi and 62~0) the total osteon area drops to about 0.30. As discussed previously, our goals of image processing are to identify and segment bone images into regions, and to produce quantitative information about these regions. Figure 4 shows results for some of the bone images tested. It can be seen that the image processing system here described has successfully segmented the bone images. Non-Haversian canals, osteons, and lamellar bone are clearly separated and are segmented as distinct regions. Due to osteonal variation, they are separated into three types, depending on whether the osteon is more mineralized (lighter) or less mineralized (darker). It is useful to distinguish between these cases, as (on a microradiograph) darker osteons often indicate that they have been formed in the bone more recently than lighter, more heavily mineralized osteons. However, there remains a small error in region classification in some of the results. This is probably due to the intensity variation around the edge of the canals and the consequent difficulties in defining the precise boundary. This can be merged with the surrounding osteons to improve accuracy further. 5. CONCLUSIONS In this paper we have discussed the traditional approaches used for bone analysis and the problems associated with their use. In order to automate the analysis process, we have developed an image processing system for rapid quantitative bone image analysis. Such a system is desirable in that it can provide more consistent and reliable results than have been obtained previously and is also capable of examining the whole of the crosssection of a bone. With this system, it is now possible to process a large number of bone images. This will enable further studies to investigate any possible correlation between chronological age of the person and the bone microstructure to be undertaken. These studies will be able to avoid many of the measurement problems found with previous manual methods. It is envisaged that such a system will also be very useful for studies of osteoporosis.
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We are currently investigating knowledge-based techniques to combine features from different bone image modalities, i.e. transmitted white light, microradiographs, plane polarized light, circularly polarized light, to utilize fully the information available for better bone image analysis. For instance, while some important features can be obtained from microradiographic images, differentiating between osteons and lamellar areas is difficult. Processing transmitted light images would be particularly useful in conjunction with corresponding microradiographic images as these images contain textural information. Such images are rather difficult to process by conventional image processing techniques including some of the newly developed algorithms in the literature. Nevertheless, we have recently developed a simple covariance methods for processing of texture [9]. Initial tests have shown that our covariance method was able to distinguish between the Haversian canals and o&eons with high accuracy. Despite the irregular appearance of bone images, there is still a great deal of order to the image. For instance, osteons are usually centred on a canal and are proportional in size to each other. Fragments of osteons are derived from previous generations that have been partially removed and replaced by new osteons. These are other features in bone images are fairly constant in their expression across divergent examples of bones and reflect underlying fundamental biological constants. In view of this, it would seem to be desirable to utilize the features inherent in the bone structure or its segmentation. Of particular interest is the extraction of circular or oval shapes from bone images. Although there is only a limited number of useful features that are present in the image, the appearances of these features can vary significantly from sample to sample. We envisage that methods will soon be developed in the near future that make appropriate use of the available knowledge of bone structures and will thereby have improved performance and practicality over existing methods of bone feature analysis. would like to acknowledge Professor AIan Boyde for providing Fig. 2(c) and 2(d). This research is supported in part by a grant from the Australian Research Council.
Acknowledgements-We
REFERENCES 1. M. R. Zimmerman and J. L. Angel (Eds), Dating and Age Determination of Biology Materials. Croom Helm (1986) 2. D. Thompson, The core technique in the determination of age at death in skeletons, J. Forensic Sci. 902914, (1979). 3. M. Y. Iscan and S. R. Loth, Osteological manifestations of age in the adult. In Reconsrruction ofLife From the Skeleton, Alan R. Liss, M. Y. Iscan and K. A. R. Kennedy (Eds), New York (1989). 4. M. J. Wang, W. Y. Wu and C. M. Liu, Performance evaluation of some noise reduction methods, Computer Vision, Graphics and Image Processing, 54, 134-146 (1992). 5. Z. Q. Liu and T. M. Caelli, A sequential recursive filter for image restoration Computer Vision, Graphics, and Image Processing 44,296306 (1988). 6. R. Paranjape, W. Morrow and R. Rangayyan, Adaptive-neighborhood histogram equalization for image enhancement, Computer Vision, Graphics and image Processing 54, 296-306 (1992) 7. R. C. Dubes and A. K. Jain, User’s dilemma, Pattern Recognition 8, 247-260 (1976). 8. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, New Jersey (1988). 9. S. Madiraju and Z. Q. Liu, On rotation invariant texture processing techniques, Proc. The 1st IEEE Inf. Conf Image Processing, ICIP’94, Vol.11, 655-659, Austin, Texas, November 13-16, (1994). About the Author-ZHI-QIANG LIU is currently a senior lecturer with the Department of Computer Science, The University of Melbourne. He received a M.A.Sc. degree in Aerospace Engineering from the Institute for Aerospace Studies, The University of Toronto, and a Ph.D. degree in Electrical Engineering from The University of Alberta, Canada. He is the leader and the Principal Investigator of the Computer Vision and Machine Intelligence Laboratory (CVMIL) at the Dept. of Computer Science, The University of Melbourne. His research interests include image processing, computer vision, neural networks and intelligent and knowledge-based systems. About the Author--TIM AUSTIN received a BE (Hons) degree in Electrical Engineering, a BSc degree in Computer Science, and Master of Engineering Science, all from the University of Melbourne. Currently, he is an engineer at Telstra Australia. About the Author-DAVID THOMAS is a Professional Officer with the University of Melbourne, School of Dental Science. His research interests include the characterisation and testing of bone
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Z.-Q. Lru et al. (with application to forensic analysis and to osteoporosis), the computer modelling of teeth and the analysis of the human face. About the Author-JOHN CLEMENT is an Associate Professor in Oral Anatomy and Forensic Odontology, at the University of Melbourne. He is also an Honorary Associate Professor (Clinical) with Department of Forensic Medicine and an Honorary Research Associate with Department of Physics at the Monash University. He is a consultant forensic odontologist in charge to the Victorian Institute of Forensic Medicine and a Victoria Police Forensic Dentist.