Bone image segmentation

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 46, NO. 5, MAY 1999 565

Bone Image SegmentationZhi-Qiang Liu,* Senior Member, IEEE, Hui Lee Liew, John G. Clement, and C. David L. Thomas,Member, IEEE

Abstract—Characteristics of microscopic structures in bonecross sections carry essential clues in age determination in foren-sic science and in the study of age-related bone developments andbone diseases. Analysis of bone cross sections represents a majorarea of research in bone biology. However, traditional approachesin bone biology have relied primarily on manual processes withvery limited number of bone samples. As a consequence, it isdifficult to reach reliable and consistent conclusions. In this paperwe present an image processing system that uses microstructuraland relational knowledgepresent in the bone cross section forbone image segmentation. This system automates the bone imageanalysis process and is able to produce reliable results based onquantitative measurements from a large number of bone images.As a result, using large databases of bone images to study thecorrelation between bone structural features and age-related bonedevelopments becomes feasible.

Index Terms—Bone image analysis, bone images, clustering,image segmentation, knowledge-based.

I. INTRODUCTION

W ITH the rapid development of computer technology,computers have become an integral part in medical

image acquisition, enhancement, segmentation, labeling, andanalysis. However, radiographic images are still examined bymedical experts manually. Examination of such images is usu-ally a repetitive and labor intensive process. Further, manualexamination of images often produces subjective results thatare highly dependent on the knowledge and experience of theexaminer. Computerized analysis and interpretation of medicalimages will not only be time and cost effective, but will alsoallow objective and reproducible results to be obtained. Thisallows comparison of results to be independent of human biasand error. Quantitative information, which can be beneficialfor in-depth understanding of images and which may notbe immediately apparent to the medical examiner in a rawor enhanced image, will be more effectively obtained witha systematic approach. The availability of computer visionsystems will also facilitate image storage, communicationof the information extracted, and correlation of results fromdifferent images. However, developing functional automated

Manuscript received November 25, 1996; revised December 17, 1998.Asterisk indicates corresponding author.

*Z.-Q. Liu is with the Computer Vision and Machine Intelligence Lab-oratory (CVMIL), Department of Computer Science, The University ofMelbourne, Parkville, Victoria 3052, Australia (e-mail: [email protected]).

H. L. Liew is with the Computer Vision and Machine Intelligence Lab-oratory (CVMIL), Department of Computer Science, The University ofMelbourne, Parkville, Victoria 3052, Australia.

J. G. Clement and C. D. L. Thomas are with the School of Dental Science,The University of Melbourne, Parkville, Victoria 3052, Australia.

Publisher Item Identifier S 0018-9294(99)03119-5.

systems for medical image analysis and interpretation has beena major challenge in the field of computer vision.

One of major areas in medical image processing is theanalysis of bone cross sections in bone biology [1]. Overthe last 30 years, the study of microscopic features in thecross section of human bones, usually of the femur, tibia, andfibula, has received increasing attention. The characteristics ofmicroscopic features in a bone cross section can be used toinfer the biological age of the bone [3], [7]. The ability to dothis accurately and reliably is useful in histological studiesof bones, such as in determination of age at death. Moreimportantly, reliable analysis of bone cross sections will playa vital role in understanding of bone growth and bone diseasessuch as osteoporosis, as developments at the microscopic levelof the bone can be observed.

Traditionally, approaches in this research involve collectinga set of bone specimens and analyzing cross-sectional imagesof these specimens obtained using some imaging techniques.As the microstructures that are of interest are very small,high magnification is necessary for identification of bonestructures. Consequently, a small bone cross section resultsin a large number of images. Thus, it is both impractical andinfeasible to manually process complete bone cross sections,let a lone to process a large number of bone samples necessaryto draw meaningful conclusions. Therefore, there is a needfor automated and reliable techniques to carry out such tasks.There have been numerous reports on the study of microscopicfeatures of bone cross sections. Since the 1930’s [8], differentmethods have been developed to analyze bone cross sectionsquantitatively, mainly with the aim of determining age fromthese measurements. These studies are based on manuallyextracting features in the bone cross sections and their analysisunder a microscope.

Automated image analysis involves first acquiring digitizedimages of the bone cross sections followed by extraction ofmicrostructures in the image and analysis of the structuresto obtain quantitative measurements. This paper presents animage processing system for effective and consistent analysisof bone images.

The rest of the paper is organized as follows. Section IIintroduces the characteristics of microradiographic bone im-ages, the imaging process used to acquire bone images thatform the database in our study, and our system for bone imageanalysis. Section II-D presents a new approach for bone imagesegmentation and quantitative analysis, that utilizes our knowl-edge about bone microstructural and relational characteristics.This system is capable of handling bone cross-sectional im-ages of different quality and ensures reliable and consistent

0018–9294/99$10.00 1999 IEEE

566 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 46, NO. 5, MAY 1999

(a) (b)

(c) (d)

Fig. 1. Example images of various modalities. (a) Microradiograph, (b)transmitted light, (c) plain polarized light, and (d) circularly polarized light.

quantitative analyses. We present some experimental resultsusing this knowledge-based approach in Section III. Finally,Section IV evaluates the system and proposes possible futuredevelopments.

II. M ETHODS

A. Bone Images

There are a number of methods for imaging bone cross sec-tions, which result in bone images with different characteristicsthat emphasize different features in bone cross sections. Someof the common bone imaging techniques are microradiogra-phy, transmitted light scans, plain polarized light scans andcircularly polarized light scans. Fig. 1 shows example imagesobtained with these techniques. The microradiographic imagein Fig. 1(a) brings out different levels of mineralization oropaqueness in the bone cross section, in the form of grey-level intensity variations. The other three imaging techniquesproduce images which have more textural properties, as can beseen in Fig. 1(b)–(d). From these textural properties, informa-tion about the microstructural features of the bone cross sectioncan be derived [5]. The microradiographic image shows themost resemblance to the appearance of a magnified bonespecimen under a microscope. These are the images that willbe considered in this study.

The important features in the bone cross section are theHarvesian canals, osteons, osteon fragments, and lamellarbone. These features are identified in Fig. 2. The Harvesiancanals, which are quite easily identified in the image, arethe black areas, and they correspond to blood vessels in the

bone. In bone biology, an osteon is considered to be theHarvesian canal surrounded by concentric layers of bone.However, in this paper, osteons regions refer to the greyregions surrounding the canals. Different intensity levels ofthe osteon regions indicates the levels of bone mineralization,where the lighter regions are more mineralized. The osteonfragments are the osteon regions that do not surround anycanals. The brightest parts in the image that do not constitutethe other regions are called the lamellar bone regions.

B. Image Acquisition

Bone images used in this research were taken from a ref-erence collection established at the School of Dental Science,University of Melbourne, Melborne, Australia. The referencecollection consists of images of over 230 post-mortem femoralbone specimens collected at the Victorian Institute of ForensicPathology (VIFP). These specimens were taken from peoplewho had died with no known diseases affecting their bones.Details of the individuals from which these materials weretaken, such as age, sex, height, weight and cause of death,were available.

Bone specimens of 2–4 cm in length were cut from the mid-shaft of the femur and fixed in 70% ethanol. These specimenswere sliced to obtain cross sections with thickness of about200 m, which were then air dried and stored flat betweenpaper lined glass slides. The thickness of these cross-sectionslices is important because a thick section will appear lighter,and hence will be interpreted as having more mineralizationleading to analysis error. Bone image samples are obtained byphotographing the slides using one of the imaging techniquespreviously described in Section II-A. The resulting photographis then placed on a high-precision computer controlled-stage fitted to a microscope and a video camera. The colorvideo camera is stepped across the whole image and magnifiedimage frames are taken, in a manner that ensures there are nooverlaps among the image frames. The field of view of thecamera has dimensions 3.5 mm by 2.5 mm. The frames aregrabbed and digitized with a resolution of 512 by 576 pixels.It requires about 100 frames to capture the entire bone crosssection. An example image frame is shown in Fig. 2.

C. Bone Image Processing

Computerized interpretation of biomedical images is prob-lematic because of statistical, structural and temporal varia-tions of objects in such images. Conventional feature extrac-tion techniques are not robust enough to handle low-resolutionand noisy images such as bone cross-sectional images. Manytechniques available often make simplified assumptions thatare unrealistic for biological images, for instance, objects arecommonly assumed to be rigid, whereas biological structuresare usually flexible and distorted. We have developed a simplesystem for bone image processing [4]. The input to the systemis a magnified microradiographic bone image acquired by theprocedure described previously. The outputs of the systemare a segmented image and quantitative measurements of thefeatures extracted from the image.

LIU et al.: BONE IMAGE SEGMENTATION 567

Fig. 2. Magnified microradiograph with features identified.

Fig. 3. Bone cross section of a femur.

(a) (b)

Fig. 4. An example result of processing a bone image using the system. (a)Original image. (b) Segmented image.

There are four main steps involved in the processing of abone cross-sectional image:

• preprocessing—adaptive neighborhood smoothing [6];• clustering— -means clustering [2];• relabeling of regions;• quantitative analysis of segmented images.

Refer to [4] for more details.Fig. 4(a) shows an example of a typical bone image.

Fig. 4(b) shows the segmentation result using the simpleprocessing system. The regions in the bone cross sectionhave been identified quite successfully, with the canals,osteons and lamellae assigned to different clusters. There are,however, noticeable segmentation errors, particularly in someprocessed images, bright patches (due to imaging artifacts)surrounding canals have been misclassified as lamellar regions[e.g., Fig. 4(b)]. These inaccuracies in segmentation are aconsequence of the simplicity of the clustering method as itrelies only on pixel grey level for segmentation. Since pixels

at different locations in the image carry different information,it would be more desirable if suchgeometrical or spatialknowledge could be extracted and used in the segmentationprocess.

D. Knowledge-Based Segmentation of Bone Images

Despite the irregular appearance of bone images, there isstill a great deal of order in the image. For instance, osteonsare usually centered around a canal and are proportional in sizeto each other. Fragments of osteons are derived from previousgenerations that have been partially removed and replaced bynew osteons. These and other features in bone images arefairly constant in their expression across divergent examples ofbones and reflect underlying fundamental biological structures.In view of this, Liu et al. [4] have proposed that it is moredesirable to use the features inherent in bone structure asknowledge to improve bone image segmentation.

This section presents a new system that makes appropriateuse of the available knowledge of bone structures and therebyimproves the system’s performance in terms of accurate andconsistent bone image segmentation and quantification.

1) System Description:Fig. 5 shows the basic frameworkfor the processing algorithm implemented. The algorithm usesthe strategy of over-segmenting the bone image into smallregions, then applies region-based segmentation methods to re-fine segmentation. Thus, the image primitives required are theinitial regions that are easily obtained using the clustering andrelabeling algorithms [4], and are used as the initial regions forfurther segmentation. The input to the system is thus a roughlysegmented bone image obtained using-means clustering, andthe output is a more enhanced segmentation of the image.This image is then used for extracting quantitative informationabout the bone cross section. Given a segmented image, asecondary process relabels the clusters, which were initiallygrouped according to grey levels, into more meaningful groupsusing additional knowledge about the image.

The first step in the secondary processing of the clusteredbone image is to extract individual connected regions in theimage. The initially clustered image, obtained by the-meansclustering algorithm, is labeled with six labels, from 0–5according to grey levels. However, these regions are oftenunconnected because there are many distinct canal, osteon,and lamellar regions in the image. These regions have to berelabeled. This involves first numbering distinct regions in theclustered image, differentiating between canal, noncanal, and


Fig. 5. System diagram.

Fig. 6. Diagram showing region parameters.

cavity (to be discussed later) regions. During this process,feature parameters are determined for the regions. Based onthe calculated feature values, regions are merged accordingto some heuristic rules. Segmentation is further improvedby detecting boundaries of individual osteons. Finally, post-processing is performed to differentiate osteon fragments fromlamellar among those pixels that have not been classified aspart of the osteon.

2) Canal and Region Extraction:The clustered image isrelabeled, where each connected canal region is assigned aunique canal number, and each noncanal region is labeledwith a unique region number. For every labeled region, anumber of parameters are calculated. The feature values thatare calculated and stored for the canal regions are the radius,diameters in and directions, and the and coordinatesof the canal center. For noncanal regions, areas, perimeter,and region labels are determined. For both types of regions, atouching parameteris calculated. Fig. 6 shows a representationof two regions, and , in a bone image, The parameters,which are defined below, are identified in the diagram.

Area of a region, defined to be the number ofpixels constituting the region. In Fig. 6, the areaincludes those pixels in black, as well as thepixels on the perimeter of the region.Perimeter of a region, the number of pixels onthe boundary of the region. The boundary is

determined based on the four-connectedness con-vention.Diameters of a region in the and direction,respectively. They are calculated as the distancebetween the leftmost and rightmost pixels, anduppermost and lowest pixels, in the region.Average radius of the region, calculated to be halfof the average of the diameters.The and coordinates of the centroid of theregion. This point is defined using the midpointsof and .Touching parameter, defined to be the numberof pixels from two regions which are adjacentto one another. For instance, in Fig. 6, be-tween regions and is two because there aretwo boundary pixels from each region that aretouching boundary pixels in the other region.The label of the region that indicates region types.The label corresponds to either lamellar bone, orone of the three classes of osteon, dark osteon, orlight osteon.

The first pass relabels regions with distinct region numbers.The second pass is made where for each region, all the regionsadjacent to it are identified as its neighbors, and the touchingparameters are determined.

3) Cavity Removal:The areas on the image that are notpart of the actual bone cross section manifest themselvesas large black areas in the image (see Fig. 3). These blackareas are referred to ascavities. During canal extraction, thesecavities are identified so that they will not be mistaken ascanals in later stages of processing. Cavities are detected byspecifying a set of thresholds on the size of canals in the image.The thresholds are determined based on inspection of a largenumber of typical bone images. A black area is considered tobe a canal if it satisfies all of the following criteria:

(1)

(2)

(3)


Fig. 7. A magnified section of a segmented image containing an osteon.

where is a valid canal region, stands for the width ofthe image, is the height of the image, is a predefinedconstant, is the number of regions initially labeled as canalregions, represent the diameters of the canaland are the areas of the canalsand , respectively,and These conditions constraint the and

diameter of a canal to be no more than a certain fraction ofthe image dimension, and the area of a canal to be no greaterthan the average area of all the canals in the image by a factor

, which determines the degree of variation allowed in thecanal sizes within a cross section.

Black cavities in the image that are detected are ignoredthereafter in image segmentation. In such a way, we are ableto minimize the errors caused by cavities in the image.

4) Region Merging:The region extraction described in theprevious section gives an over segmentation of the image, asregions do not necessarily correspond directly to the actualstructures in the cross section, such as a particular osteon.To refine the segmentation, the regions extracted are mergedbased on a few heuristic rules established according our priorknowledge about microradiographic bone images.

• Pixels within a specific osteon region should have thesame or similar intensity level.

• Different osteon regions can contain pixels of differentintensities where lighter osteon regions will correspondto more mineralized osteons, or osteon fragments that donot surround any canals.

• The shape of an osteon should be relatively regular,expanding from the shape of the canal contained inside.

• The region immediately surrounding a canal must be anosteon region.

• An osteon region is usually surrounded by lamellar pixels,unless it is touching another osteon.

One of the major problems in the segmented images ob-tained using the clustering algorithm [4], is the existence ofwhite patches around some canals, as shown in Fig. 4(b). Thiseffect is magnified and shown in Fig. 7. This causes errors asthese pixels are considered as lamellar pixels in the analysis.To solve this problem, the regions surrounding the canal areexamined to ensure that only osteon regions are adjacent to thecanal. A region corresponding to one of these white patches,that has been misclassified as a lamellar region, is merged withone of the osteon regions adjacent to it. The candidate region tomerge with is determined by selecting the neighboring regionwith the highest touching parameter . This is done for allcanal regions, removing any white patches immediately aroundthe canals.

To further establish a uniform intensity within an osteonregion, any osteon region that is completely surrounded byanother osteon region is merged, where the label for themerged region is the label of the larger region. To determinewhether an osteon region is surrounded by any one ofits neighboring regions , the perimeter of the region iscompared with the value between region and . If thisvalue exceeds the perimeter of the region concerned, thenregion is considered to be surrounded by region.

For each merging operation, the , , , and of theregion are calculated. The smaller region that is merged iscalled thechild region, while the larger region that becomesthe new merged region is called theparent region. For a givenchild region , that is merged with a parent region, theparameters are updated as

(4)

(5)

where and are the areas of the parent and child regions,respectively, and are the perimeter of the parent andchild regions, respectively, and is the touching valuebetween the two regions.

These merging operations are reiterated for all the regions,until no changes are made during an iteration.

5) Osteon Region Extraction:The regions obtained afterthe merging process in some cases still do not corresponddirectly to the actual microstructures, such as distinct osteons,in the bone cross section. In order to distinguish individualosteons in the cross section and label them as separate regions,it is critical to determine osteon boundaries. This can be donewith the knowledge that osteons, with the exception of osteonfragments, usually surround a canal. An approach has beendeveloped, which is based on the idea of expanding from eachcanal in the image, labeling pixels to belong to a particularosteon, until the border of the osteon is reached, which isdetermined by some heuristics. The region merging processis not able to resolve this problem completely, because thesewhite patches are not alwaysimmediatelyadjacent to canals.Therefore, to further remedy this problem, a threshold is set forthe minimum size of osteon areas. Using this threshold, a pixellabeled to be a lamellar pixel, within this distance to a canalboundary is assumed to be part of one of the misclassifiedwhite patches, and is relabeled to be an osteon pixel. Whena lamellar pixel is encountered outside this distance, whileexpanding from a canal, it is assumed that the border of theosteon region has been reached.

Using the diameters of the canal, determined during canalextraction, the size of the neighborhood is defined. For osteon

and canal , the initial size of the neighborhood is definedas follows:

(6)

(7)

where and correspond to the and radius of theneighborhood, and is a constant which determines the sum


Fig. 8. Diagram of illustrating variables involved in clustering of a pixel.

of the and radius of the neighborhood. The value ofcan be varied to control the rate of expansion from the canals.A higher rate of expansion may result in large error whenthe neighborhood is expanded over the boundary of smallosteons too quickly, resulting in over-expansion of osteons.If lower rate of expansion is used, more iterations are neededto determine all boundaries, and there will be the problem ofnot reaching the boundary of an osteon if there is a large noiseregion within the osteon. This means that the rate of expansionis dependent on the size of the osteon. This information is notknown in advance. After processing a number of images, ithas been found that the value of in the range 5–20 givesreasonable results.

The neighborhood is initially defined to have radius (fromcanal boundary) of in proportion to the length ofthe canal diameters, . Expansion of the neighborhoodon each iteration is at rates also proportional to thediameters of the canal. The neighborhood is divided into foursubsection, where two subsections (top and bottom) containpixels within from the canal boundary and are expandedat a rate corresponding to , and the other two subsections(left and right) contain pixels that are within distance fromthe canal boundary and are expanded at rate corresponding to

. If the radius of an osteon during anth iteration, that is theaverage distance of the current osteon boundary to the canalboundary, is , then the radius of the neighborhood on the

th iteration is grown to and .Each iteration of the algorithm consists of the following

operations. Pixels are expanded recursively in the eight-connected neighborhood, from the canal center until theboundary of the canal is reached. Then the clustering processis performed within the current neighborhoodof the canal.This involves calculating for each pixel within a scorebased on the pixel’s label and its location in. The basicidea is that the closer the pixel is to the canal the higher thepixel will score. The score for a pixel, is defined by

(8)

(9)

(10)

(11)

where is defined in terms of which correspond tothe labels of region belongs to, and the average label ofthe osteon region surrounding canal, respectively. is theEuclidean distance between the pixeland the center of canal. The constant weights and are adjusted to rate the

effect of the two features in the score. In this paper, bothare given equal weighting. For each canal, the values for

and are computed as pixels are added to the clustercorresponding to the osteon surrounding. These variablesare identified in Fig. 8.

After computing the score for a particular pixelin neigh-borhood of canal , there is a number of possible situations.

• The pixel may not have been clustered yet. In this caseit is assigned to the current osteon cluster correspondingto canal , and its score is stored.

• The pixel may have been assigned to another osteonalready. In this case, the current score is compared withthe score that is associated with which indicates itsdegree of membership in the current cluster. If the currentscore is higher than the old score of the pixel, then thepixel is reassigned to the current cluster, and the newscore is stored. Otherwise the expansion is halted in thisdirection, since this situation indicates the expansion hasmet another osteon region. This means that a pixel at theboundary of two osteons with the same label is assignedto the osteon region closest to it, whereas a pixel at theboundary of two osteons with the same distance fromtheir corresponding canals is assigned to the cluster witha label most similar to it.

• If the pixel has been previously assigned to the currentosteon, then the score is updated, because it may havebeen changed due to the changes in the value of theaverage label and radius of the osteon.

6) Postprocessing:The final segmented image is obtainedby relabeling the regions using the original range of labels1–5, corresponding to the five classes of features in the bonecross section. Pixels that have not been assigned to an osteon,and are not part of a canal or cavity, are labeled as osteonfragments if they have osteon labels to begin with, otherwisethey are labeled as lamellar pixels. A simple mean smoothingis performed to remove noise pixels and smooth the boundariesof canals and osteons in the image.


(a) (b)

(c) (d)

Fig. 9. Typical results of different steps: (a) original image, (b) canal map,(c) merged regions, and (d) segmented image.

(a) (b)

Fig. 10. Segmentation: (a) the original image and (b) the segmented image.

Fig. 9 gives a step-by-step illustration of an image beingprocessed using the techniques described in this section.

III. RESULTS

The following sections present and discuss some resultsobtained using the techniques proposed in Section II-D.

At the first stage, bone image samples are preprocessedand clustered. This results in segmented images with featurescorresponding to the five bone microstructures. Fig. 10 showsa processed bone image.

(a) (b)

Fig. 11. Comparison of segmentation process: (a) manual segmentationshown by the contours drawn and (b) automated segmentation by the systemproposed.

From the processed images, it can be observed that the whitepatches that were apparent in segmented images using-meansclustering, as that shown in Fig. 4(b), are no longer present.Furthermore, each osteon region surrounding a canal has beenidentified and labeled consistently as one of the three classesof osteon regions: dark osteon, osteon, or light osteon. Thelabel of each osteon region was determined by averaging thelabels of the pixels composing the osteon. In other words, anosteon’s label is determined by the label of the majority of thepixels it contains. This labeling process worked effectively(see Fig. 10).

To further demonstrate the effectiveness of the proposedsegmentation technique, Fig. 11(a) shows an image in whichthe osteon regions have been identified by boundaries drawnmanually by a bone biologist. Fig. 11(b) shows the sameimage segmented using the technique proposed in this paper.A close inspection of Fig. 11(b) shows that the relativelyuniform darker grey areas (the osteon regions) surroundingthe canals are similar in size and shape to those regionsin Fig. 11(a) that are delineated manually. This result isparticularly striking when compared with the segmentationresult shown in Fig. 4(b), where most osteon regions havebeen misclassified. In general the proposed algorithm is ableto reliably identify the osteons and canals.

The segmented images were analyzed to produce quantita-tive measurements about the composition of the bone crosssection. There are six measurements obtained for each image.Five of the measurements are the percentage of the imagearea (in number of pixels), excluding the cavity area, occupiedby pixels from each of the five types of regions, namely,canal (canal), dark osteon (dkost), osteon (ost), light osteon(ltost), and lamellar (lamellar). One additional measurementis obtained by summing the percentage area occupied by allosteon regions (totost).

At the School of Dental Science in the University of Mel-bourne, we have collected over 230 post-mortem femoral bonespecimens from which we selected samples from differentage groups. A database of bone images were then generatedaccording to their age groups. A total of eight age groups wereformed ranging from 5–95 years. The measurements obtainedfor images in each of the age groups were averaged. As


TABLE IPERCENTAGE AREAS OF THEMICROSTRUCTURES IN THEBONE IMAGES

an illustration, Table I shows the measurements. From thesemeasurements correlation of bone microstructure changes withage may be made.

IV. CONCLUSIONS

Bone image analysis is a major research topic in bonebiology and related areas. Traditionally bone image analysisis carried out by error-prone and tedious manual processes.As such, it is impractical, if ever possible, to process entirebone cross sections. This means that inferences have to bedrawn from a very small number of bone specimens. As aconsequence, such studies are not conclusive and, in manycases, produce conflicting results.

Recent advances in imaging technology and digital imageprocessing have offered us powerful tools for developingfunctional image analysis techniques for bone image analy-sis. This opens up opportunities for large scale bone imageprocessing that will provide researchers and medical doctorsin bone biology with far more reliable and conclusive results.However, as bone images are normally poor in contrast andnoisy, important features such as osteons are not well definedin the image. To process images of such quality represents asignificant challenge. Further, such a system is desirable inthat it provides more consistent and reliable results than thoseobtained manually.

The human expert relies heavily on the structural andrelational information in his/her manual analysis. This impliesthat knowledge plays a crucial part in the analysis processand that a more useful system will have to beknowledge-based. In this paper we have proposed a new image analysissystem for quantitative bone image analysis, that uses suchspatial knowledge as relationship between canals and osteons,expected shapes of osteons, and the extent of osteons. Theuse of knowledge about bone cross section characteristics andmicroradiographs has enabled us to obtain quantitative analysisresults with greater accuracy and consistency. The resultspresented in Section III show considerable improvements ascompared to our previous pixel-based algorithm that uses asimple clustering technique for segmentation [4]. This systemprovides reliable quantitative measurements that enable us toestablish the correlation between changes in microstructuresand in age.

With this system, it is now possible to process a largenumber of bone images. This will enable further investigationsof possible correlations between chronological age of theperson and the bone microstructure. These studies will be

able to avoid the measurement problems encountered in themanual process. It is envisaged that such a system will alsobe a valuable tool for the study of osteoporosis and otherage-related bone diseases.

To utilize fully the information available for better boneimage analysis, investigation ofknowledge-basedtechniquesto combine features from different bone image modalities1

is currently underway. For instance, while some importantfeatures can be obtained from microradiographic images, dif-ferentiating between osteons and lamellar areas is difficult.Processing transmitted light images would be particularlyuseful in conjunction with corresponding microradiographicimages, as these images contain textural information [5]. Suchimages, however, are rather difficult to process by conventionalimage processing techniques including some of the newlydeveloped algorithms in the literature.

REFERENCES

[1] J. Ahlquist and O. Damster, “A modification of Kerley’s method forthe microscopic determination of age in human bones,”J. Forensic Sci.,vol. 14, pp. 205–212, 1969.

[2] A. K. Jain and R. C. Dubes,Algorithms for Clustering Data. Engle-wood Cliffs, NJ: Prentice-Hall, 1988.

[3] E. Kerley, “The microscopic determination of age in human bone,”Amer. J. Physical Anthropol., vol. 23, pp. 149–163, 1965.

[4] Z. Q. Liu, T. Austin, C. D. L. Thomas, and J. G. Clement, “Bone featureanalysis using image processing techniques,”Comput. Med. Biol., 1995,pp. 487–494.

[5] Z. Q. Liu and S. Madiraju, “A covariance-based approach to textureprocessing,”Appl. Opt., vol. 35, no. 5, pp. 848–853, Feb. 10, 1995.

[6] W. M. Morrow, R. M. Paranjape, and R. Rangayyan, “Adaptive neigh-borhood histogram equalization for image enhancement,”GraphicalModels Image Processing, vol. 54, no. 3, pp. 259–267, May 1992.

[7] D. Thompson, “The core technique in the determination of age at deathin skeletons,”J. Forensic Sci., 1979, pp. 902–914.

[8] M. R. Zimmerman and J. L. Angel, Eds.,Dating and Age Determinationof Biological Materials. Provident House, Beckenham Kent, U.K.:Croom Helm, 1986.

Zhi-Qiang Liu (S’81–M’82–SM’91) received theM.A.Sc. degree from the Institute for AerospaceStudies at the University of Toronto (UTIAS),Toronto, Canada, in 1983 and the Ph.D. degreein electrical engineering from the University ofAlberta, Alberta, Canada, in 1986.

He is currently an Associate Professor with theDepartment of Computer Science and SoftwareEngineering, The University of Melbourne, Mel-bourne, Australia. Before joining The Universityof Melbourne, he worked in communications

industry in Canada as a Principal Engineer. His interests include camping,computer vision, gardening, fuzzy-neural systems, and intelligent informationmanagement in the Internet, beach walking, and software development. Hehas numerous publications in the relevant research areas.

Dr. Liu has received a number of prestigious scholarships and fellowshipsand is on editorial boards of several major international journals.

Hui Lee Liew, photograph and biography not available at the time ofpublication.

1There are several major imaging techniques available which revealsdifferent aspects of the bone cross section, namely, transmitted white light,microradiographs, plane polarized light, and circularly polarized light.


John G. Clementreceived the B.D.S. and Ph.D. de-grees from University College of London, London,U.K., the L.D.S. degree from the Royal College ofSurgeons, U.K.; the Dip.For.Odont. from LondonHospital Medical College, London, U.K.

He is Associate Professor in Oral Anatomy in theSchool of Dental Sciences, the University of Mel-bourne, Melborne, Australia. His research interestsinclude forensic facial reconstruction, forensic oste-ology, bone biology, and the management of massdisaster scenes. In 1989 he initiated the Graduate

Diploma in Forensic Odontology at The University of Melbourne. He is co-designer of a computer program Disaster And Victim IDentification (DAVID)which mimics the internationally acknowledged standardized paperwork ofInterpol in an electronic form. He is co-editor ofCraniofacial Identificationin Forensic Medicine(New York: Oxford Univ. Press, 1998).

C. David L. Thomas (M’96) received the HigherNational Certificate in applied physics from ReadingCollege of Technology, Reading. U.K., in 1972, aGraduate Diploma in digital computer engineeringfrom the Royal Melbourne Institute of Technol-ogy, Melbourne, Australia. in 1985 and the M.Eng.(IT) degree from the same institution in 1993. HisM.Eng. research was on the application of Fouriershape descriptors to biological form.

He has been employed in the School of DentalScience, University of Melbourne since 1980. His

current research interests are in bone biology, the numerical modeling of facesand in the mechanical characterization and numerical model ling of teeth.

Documents

Bone image segmentation