Hand Bone Radiograph Image Segmentation With ROI Merging

Transcription

1 Hand Bone Radiograph Image Segmentation With ROI Merging TRAN THI MY HUE, JIN YOUNG KIM, MAMATOV FAHRIDDIN Department of Electronics Engineering, Chonnam National University, Gwangju, KOREA. Abstract: Bone segmentation in radiographic imaging is an intermediate level processing stage for an automated vision system for the skeletal assessment of children. It is one of the challenging problems in medical image analysis due to high noise levels and low contrast with non-uniform and complex intensity distribution of radiographic image. In this paper, we present a local merging algorithm for automatically segmenting bones from the hand radiograph. With an initial over-segmented image, in which the many primitive (homogeneous) regions are generated by watershed transform and image pre-processing, the hand bone X-ray image segmentation is performed by the local merging process on regions of interest (ROIs). Firstly, the hand is separated from the background to get hand boundary. In this phase, aiming the hand separation coincide with the region reduction, an merging algorithm based on the region adjacent graph (RAG) and nearest neighbour graph (NNG) is proposed. Next, the curvature information of the hand boundary is analyzed for determining the desired ROIs on the hand image. Finally, the sub-rags which are sub-graph of the RAG associated with the ROI are extracted, and the local merging process on each sub-rag is individually executed. Experiments are carried out on 30 hand X-ray images of the young children where the carpal bones have distinct, non-overlapping boundaries. The experimental results show that with the proposed method, an accurate and robust segmentation can be achieved. Key-Words: hand bone segmentation, nearest neighbor region merging, watershed transform 1 Introduction Hand-bone segmentation is an interesting area of medical image processing. It is one of the main steps of bone age assessment, bone mass assessment, and bone fracture examination. Bone segmentation, especially hand bone segmentation, is a challenging problem in medical image analysis and classification domain. The accuracy of the assessment system depends on the detailed shape analysis of the bones of interest, which means that a complete and precise segmentation of these bones is needed. Various attempts towards this objective have been reported with varying degrees of success. Model based approach [1]-[2] with a prior knowledge on the shape of the bones, edge-based [3] and active shape models approach in [4] were used to segment hand bones. However, the complete segmentation of hand bones is an extremely difficult task, as the skeletal structure in the hand is complex, the bone contours are not well defined and there is a great variability between different subjects. Thus, the above proposed methods either produced lukewarm results or needed expert human intervention. This paper presents an algorithm for automatically segmenting the bones of interest based on the local merging procedure on the regions of interest of the hand X-ray image. Watershed transform [5]-[6]-[7] has proven to be a very useful and powerful tool for image segmentation. Recently, the watershed transform is becoming more and more popular in such different computer vision areas as biomedical or medical image analysis [8], and image processing [9]. In this work, watershed transform will be utilized to initialize the set of homogeneous primitive regions to serve the local merging process. However, the direct application of the watershed transform on the image will produce an over-segmentation result. As an image preprocessing including edge preserving noise reduction and gradient magnitude thresholding is used to reduce over-segmentation. After preprocessing, the proposed ROI merging method will be performed for separating the bones of interest from the soft-tissue. In our work, the graph theoretic approaches such as region adjacency graph (RAG) and nearest neighbor graph (NNG) are utilized. Nodes of the graph represent regions in the image and edges connect each pair of neighborhood regions. The existent methods [10]-[11], the merging of the pair of neighbourhood regions was decided by searching the feature on global graph. The large number of primitive regions obtained ISBN:

2 from the watershed algorithm together with the complexity of intensity distribution of soft-tissue from the distal bone to ulna bone of the hand X-ray image are drawback. They make to increase the probability of the wrong decisions during the merging process. Therefore, we propose to split the RAG graph into the different sub-graphs. In this way, each sub-graph is associated with regions of interest (ROIs) of the hand X-ray image. To extract the ROIs of the image, the hand is separated from the background to obtain the hand boundary. Next, based on the hand shape and curvature information of the hand boundary the desired ROIs of the image are extracted. After that, the sub-graphs of RAG which represent the ROI are determined. Last of all, the merging on each sub-graph is independently executed to get the final segmentation result. The experimental results show that our approach can provide bone recognition accurately. In the next sections the details of the proposed method are outlined and described. Section 4 presents sample results obtained through the application of the proposed algorithm. Finally, Section 5 draws the conclusions. 2 Image Preprocessing Watershed algorithm based on immersion simulation [5], which is a method to construct the watershed line by considering grey-tone of an image as altitude of a topographic surface and then flooding the image from the regional minima, produces coherent over-segmented regions preserve most structures of the interest object. However, the watershed algorithm is very sensitive to noise and thus leads to severe over-segmentation. Therefore, a preprocessing including edge preserving noise reduction and gradient magnitude thresholding is utilized to reduce over-segmentation. Hand bone images can be taken by X-ray. Due to various factors, these images are generally poor in contrast and they are often blurred and contain noise by artifacts. For this reason, an anisotropic diffusion filter [12] that smoothes noisy regions in the image while respecting edge boundaries is utilized to remove the blur and noise. Next, the thresholded gradient magnitude image G T [10] is obtained as follows. G T (p) = G(p) if G s (p) > T 0 otherwise (1) where p indicates pixel of image, G is gradient magnitude image based on Gaussian derivative filter [13]. G s is the image which is produced by smoothing only the non-candidate edge pixels in G. A candidate edge pixel is defined as having intensity value in G that is a local maximum along the direction of the gradient vector at that pixel. In hand radiograph, we can easily identify three entities: background which is of uniform grey level and of the lowest intensity, soft-tissue which is of nonuniform grey level due to the varying thickness and brighter than background but locally (i.e. in a local neighborhood) less bright than bone, bone which is of non-uniform grey level and locally brighter than soft-tissue. Therefore, the choice of the low threshold T is deliberated in order to guarantee that erroneous merging of bone with tissue does not occur. In our implementing, threshold T is set to μ 2 with μ is the global mean intensity of the image. This threshold provides a good compromise between the satisfactory initial over-segmentation reduction and the preservation of the image contours. Finally, watershed transform is applied to the thresholded gradient magnitude image to obtain the primitive regions. Fig.1 shows an example. Fig.1(a) is the hand X-ray image, 1(b) is the anisotropic smoothing of the image, 1(c) is the gradient magnitude image using Gaussian derivative filter, 1(d) is the watershed segmentation without threshold. Clearly, there is a severe oversegmentation in Fig.1(d). Such small regions are not reliable for calculating the region statistics and they will also increase the computational cost in our region merging algorithm. Fig.1(e) is the watershed result with threshold T = μ 2. We see that the over-segmentation is significantly reduced, while the contours of the bones are well preserved. 3 Hand Bone Segmentation The pre-processing reduces the primitive regions considerably, especially the background area. In this phase, the proposed method includes two main stages: ROI extraction and ROI merging. For ROI extraction, the first the merging algorithm is proposed to separate the hand from the background coincide with the region reduction. Next, 7 ROIs correspond to 7 areas of the hand including 5 ROIs contain the phalange bones, two remaining ROIs contain the metacarpal, carpal, ulna and radius bones are extracted. Finally, the ROI merging which is the local merging on each ROI is performed to get the final segmentation result. 3.1 ROI Extraction In pre-processing, the slow threshold is applied to reduce the initial regions and preserve the bone ISBN:

3 contours. However, this threshold is not high enough to remove all unnecessary regions in the background area (see Fig.1(e)). Thus a separating process is needed in order to split the hand from the background. Different methods [14]-[15] have been a) b) c) d) e) Fig.1 a) Original image, b) anisotropic smoothing of image, c) gradient magnitude image, d) The initial regions of watershed without using threshold (6379 regions), e) the initial regions of watershed with threshold T = μ 2 (1047 regions). done to split the hand from the background of the radiograph image. However, in our problem to drive to the hand separation with the region reduction, a proposed merging algorithm is utilized. Then the desired ROIs on the hand are extracted The Hand Separation We propose a merging algorithm which based on measuring the dissimilarity between pixels along the boundary of two regions to separate the hand from the background. For the convenience of expression, we use the definition of the region adjacency graph (RAG) and the nearest neighbor graph (NNG) [10] for representation of an image. RAG is an undirected graph which can be expressed as G = (V, E), in which V = {1, 2,, K} is the set of nodes and E VxV is the set of edges. Every region of the image corresponds to a node of the graph, and an edge exists if two nodes are neighbors. Each edge v i, v j E has a corresponding weight w v i, v j to measure the dissimilarity of the two nodes connected by that edge. A merging cost function S expresses the weight w between two nodes of graph is formulized as follows. S(R 1, R 2 ) = 1 B μ L 1(c) μ L 2 (c) 2 c B 1 + B I(c) (2) c B Where I indicates the image, c indicates the pixel of the image, B is a set of the pixels belong to common boundary between region R 1 and region R 2, B represents the pixel number of B, L k (c) = {p = (x, y) R k p c r, c B} is the set of pixels belonging to region R k limited by a circle with center c B and radius r. And μ L k (c) corresponds to the mean intensity value of L k (c). With a given specified RAG and the merging cost function S, its corresponding NNG is a directed graph G m = (V m, E m ) where V m = V and directed edge (i, j) V belongs to E m if S(i, j) = min{s(i, k): (i, k) E}. The edge start at a node is directed toward its most similar neighbor. In particular, if the beginning and ending nodes of a path are superposed, there exists a cycle. An example of RAG and NNG is shown in Fig.2. It is easy to verify that the NNG has the following properties [10]. (1) NNG contains at least one cycle. (2) The maximum length of a cycle is two. (3) The regions of the most similar pair are connected by a cycle. (4) A node can participate at most one cycle. (5) The maximum number of cycles is V 2. The algorithm for the hand separation and the region reduction is presented by the iterated merging process. The algorithm starts from the initially segmented image obtained from the preprocessing. At each iteration, the candidates for the merging are considered and only a desired candidate is chosen to merge. A pair of regions is called a merging candidate if they are the most similar neighbors in each other s neighborhood. The property (3) shows that all cycles of the given NNG graph represent the candidates of the merging process. In order to guarantee that the hand will be ISBN:

4 separated from the background, at each loop we perform to merge a pair of regions corresponding to the cycle has the minimum mean intensity in all existent cycles of the NNG graph. The algorithm is described in Table1. threshold can be completely removed. And we can split the hand from the background. At the end of the algorithm, number of regions in the hand are also reduced considerably (see Fig.3a). The hand separation is a necessary step to serve the automatic ROI extraction. Fig.2 An example of region partition ( left ), the corresponding RAG and one of its possible NNG (right) Table 1. Algorithm1. Hand separation and Region Reduction Input : RAG and NNG of N-partition Output : The region merging result with the hand is separated from the background (in the form of RAG of N/2-partition). 1. Set i = For the NNG in the i th graph layer, find the minimum weight edge of the RAG using the cycles. 3. Let a set of cycles T i i, R k2 forms i = R k1 i, R k2 k i 1, k 2 [1, n], R k1 a cycle in i th NNG 4. Calculate set of mean intensity values U i = μ(x) x T i 5. Find and merge the cycle x min T i if μ(x min ) = min U i 6. Update the RAG, NNG and the cycles 7. Set i = i Go back to step 2, until RAG remains N/2 partition 9. Return RAG We know that the background area is smaller intensity than the hand area in the hand radiograph images. Thus, the method prior merges the candidate which has the small mean intensity value, it ensures that the pairs of regions in the background are merged earlier than in the hand area. Together with the N 2 merging times, the unnecessary regions in the background which can not be vanished by the a) b) Fig.3 a) The hand separation and the region reduction result, b) the hand boundary (red boundary) Automatic ROI Extraction In this section, 7 ROIs correspond to 7 areas of the hand are extracted. To partition the hand into different ROIs, thirteen landmarks are expected for a normal hand: five at the fingertips, four between the fingers, two at the corner of the wrist and two where the wrist intersects the edge of the image (see Fig.6). From the result of the hand separation, the hand boundary can be obtained (see Fig.3b). The methods [15]-[16] proposed finger region localizing method use information related to the position and the direction of the hand in the image. However, these methods require that the tip of the middle finger be located on the highest position of the hand, but this condition may not be satisfied because of the rotation between [90 o, -90 o ] of the hand shown in the Fig.4. Therefore, we utilize the curvature information with robustness against variation of the direction of the hand and fingers for extracting the landmarks [17]. Taking into account the hand boundary chain as a digital curve, an algorithm for curvature estimation of digital curve [18] is applied to get the curvature information. The algorithm based on Vialard s method [19] to detect the discrete tangents of curve. With the obtained discrete tangents, the curvature value of the boundary hand is estimated. The first, the landmarks on the tips and valleys are extracted by analyzing the curvature of hand shape in Fig.5. The points of arch parts at the tips and the valleys have a small radius of curvature R value, but other points might have R value close to ISBN:

5 . It means that the tip and valley landmarks have larger curvature value than other points. Fig. 6 shows the finger tip and valley landmarks using the curvature estimation landmarks from (1) to (9). To localize the finger area, it is necessary to define additional points on the boundary of the hand of the first, second and fifth finger. For the fifth finger, it has points (1) and (2) which are landmarks on the hand boundary in Fig.7. From the point (2), we determine a tangent (d) of curve (C) which get (1) as Fig.7 Construction of the additional point. Fig.4 Cases with the top position of the hand located on the fourth finger. Fig. 5 Radius of curvature of finger shape Fig. 8 Landmarks and the additional points. We can see that the ROI in Fig.9a does not cover the proximal bone. Therefore, an ROI adjustment is needed to get the desired ROI which contains phalange bones inside. For this correction, an additional point (1b) between the proximal and metacarpal bone is detected (see Fig.9b). On horizontal line which is drawn by the center of line (2a-2) and the tip point (1), a vertical edge with the start point at center of (2a-2) and prolong to metacarpal bone is extracted in Fig. 9(b). And the point (1b) is detected by seeking intensity changes along the vertical edge and computing the average points among nearby intensity changes [20]. With the tip landmark, the point (1b) and two tangents, we can obtain the desired ROI as shown in figure 9(c). Fig.6 The extracted feature points of Fig.1a. a curve center. Then we extract a point (2a) which satisfies the condition that the angle between tangent (d) and line (2a-2) is 90 o. Other additional points (8a) and (6a) for the first and second finger can be found in the same manner (see Fig.8). And the ROI of the finger is determined as follows : For the fifth finger, it has point (1) is finger tip, (2) and (2a) are the valley points. A arch part (C) which gets tip (1) is center (see Fig.7) is extracted. Then the left tangent and right tangent which pass through valley points (2a) and (2), respectively, and touch the curve (C) are determined. After that, polygonal region of interest of the fifth finger can be localized to base on two tangents as shown in Fig.9(a). a) b) c) Fig.9 a) Polygonal region of interest of the fifth finger, b) Extracting the additional point (1b), c)the desired ROI after correcting. The ROIs for the first, second, third, fourth finger are constructed in the same manner. Finally, two remain landmarks which located at the corner of the wrist are extracted to get the remaining ROIs. We perform to analyze curvature information on the boundary curves (A-2a) and (B-8a). With each curve, landmark corresponds to point has the largest curvature landmarks (10) and (11) in Fig.6. And ISBN:

6 two remain ROIs are extracted as shown in Fig. 10. Fig.10 ROIs imposed on the region reduction result image. which corresponds to the cycle of the sub-nng, and pair of regions are not the bone candidate. To test the existence of the candidate bone in a pair of regions, we define a function P as follows. P(R 1, R 2 ) true (R 1 R 2 and N(R 1 ) = 1) = or (R 2 R 1 and N(R 2 ) = 1 ) (3) false otherwise Where N(R k ) indicates the number of neighbourhood of region R k. Table 2. Algorithm 2. The ROI merging algorithm 3.2 ROI Merging After obtaining the ROIs, the hand bone segmentation is performed. As above mention, the definitions of the RAG and NNG graph were utilized for presenting the image. From the RAG graph obtained by the region reduction phase, the sub-rags present the ROIs are extracted. A sub- RAG is sub-graph of the RAG graph and has the same properties as the RAG. The nodes of each sub- RAG are formed by the primitive regions located inner or cross the border of the corresponding ROI. We notice that since the background region is separated in the previous stage, it is not considered for constructing the sub-graph. With a specified sub- RAG and the merging cost function S which was defined above, we also obtain a corresponding NNG. It is easy to express the algorithm, in the next parts we will mention the NNG of the sub-rag as sub-nng. In order to arrive at the final segmentation result, a local iterated merging on each sub-rag is proposed. For the merging problem, a stopping condition is very important to terminate the merging procedure. In previous approaches [10], the stopping criteria of the merging were proposed by using the prior knowledge such as a given number of regions is reached or the merging cost exceeds a pre-defined threshold. These criteria are very difficult to apply for our problem where the number of bone objects depend on the ages and the intensity distribution on the soft-tissue area is heterogeneous. Therefore, we propose a stopping condition of the local merging based on the combination of the RAG, sub-rag and sub-nng. In the context of region merging, a region is defined as a bone candidate when it located inside an another region and does not connect to any regions. For our problem, the merging predicate will decide whether there is an evidence of merging between the pair of regions, it involves to two aspects: a pair of regions is a merging candidate Input : RAG of N- partitions which is output of algorithm 1, sub-rag and sub-nng of 7 ROIs Output : the final segmentation result contains the bone candidates (in form of RAG ) Parameters : N = 7 : the number of ROIs 1. set i = 1 2. For j = 1 to N 2.1. The j th sub-nng in the i th iteration, find the minimum weight edge of the j th sub- RAG using the cycles If P between the cycle is false Merge the corresponding pair of regions and update the j th sub-rag, j th sub-nng and RAG 2.3. Else Remove the bone candidate region in the j th sub-rag and update the j th sub-nng, no change RAG. 3. i = i Go back to step 2, until the number of regions in each sub-rag is one. 5. Return RAG. In our algorithm, at each loop, the cycles of each sub-nng are considered. If pair of regions of the cycle are not the bone candidate, the merging is occurred and sub-rag, sub-nng, RAG are modified. Otherwise, just remove the bone candidate from the sub-rag, and preserve it in RAG. According to above property (1), at each iteration there is at least a cycle of sub-nng is considered, as at least a region is vanished from the sub-rag. The iteration stops when the remaining number of regions in each sub-rag is one. As a result, at the end of the algorithm all regions are merged exception the bone candidates which are stored in RAG. The algorithm is described in table 2. With the local merging algorithm, the desired bones (the bone candidates) of each ROI are obtained. And the end of algorithm, the bones of ISBN:

7 interest of the X-ray image are recognized. To get the full soft-tissue region, we perform to combine the tissue parts of the ROIs (see Fig. 11). algorithm. The desired ROI extraction based on the analysis of the hand boundary feature, the representation of ROIs by the RAGs, NNGs with the local merging process on each ROI individually, demonstrate the robust and efficient for segmenting hand bones. The experiments are carried out on hand bone X-ray images and the results are very promising. a) b) Fig.11 a) The ROI merging result image, b) the bone contours superimposed on the original image in Fig.1a. 4 Experiment Results In this section, a few of the results obtained by the proposed algorithm are presented. Figure 12 contains examples that illustrate the stages of the segmentation algorithm and visually evaluate the quality of the segmentation results. Row (a) is the original hand bone X-ray images. Row (b) the primitive regions after the hand separation and region reduction with the imposed ROIs. In preprocessing stage, the gradient magnitude image obtained by Gaussian derivative filter with σ = 1.5. At the end of the ROI merging algorithm, the desired bones of the hand image are recognized. The existence of some artifacts in the soft-tissue may be generate some small regions which are not the bone in the final result image. However, these undesired regions can be removed by analyzing the vertical axes along the recognized large bones such as phalanges, metacarpals, carpals, radius and ulna. Row (c) shows the final segmentation result with hand bones are detected from the soft-tissue, (e) region contours are superimposed on original image. The experiment results on 30 hand images of the young children demonstrate the accurate and robust segmentation of our algorithm. a) b) c) d) ) 5 Conclusion The complexity of skeletal structure in the hand, uneven contrast, blurring and noise by artifacts make very difficult to realize an automatic bone segmentation. In this work, we have developed a fully automatic methodology to the hand bone X-ray segmentation that includes the ROI extraction algorithm and the local merging algorithm. Watershed algorithm and the pre-processing using anisotropic diffusion filter, the gradient magnitude thresholding are used as the point starting of our Fig.12 a)the original Image, b) The pre-processing result and ROIs, c) The final segmentation result, d) the bone contours superimposed on the original image. 6 Acknowledgements This research was supported by the MKE ( The Ministry of Knowledge Economy), Korea, under the ITRC ( Information Technology Research Center ) support program supervised by the NIPA (National ISBN:

8 IT Industry Promotion Agency) (NIPA C ). References: [1] D. J. Michael, and A.C.Nelson, HANDX: A model-based system for automatic segmentation of bones from digital hand radiographs, IEEE Trans. On Medical Imaging, Vol.8, No.1, 1990, pp [2] N. D. Efford, Knowledge-based segmentation and feature analysis of hand wrist radiographs, School of Computer Studies, University of Leeds, Report 94.31, Research Report Series, [3] B. S. Sharif, S. A. Zaroug, E. G. Chester, J. P. Owen and E. J. Lee, Bone edge detection in hand radiographic images, Engineering in Medicine and Biology Society (EMBC). Proceedings of the 16th Annual International Conference of The IEEE, Vol.1, 1994, pp [4] F. Vogelsang, M. Kohnen, H. Schneider, F. Weiler, M. W. Kilbinger, B. B. Wein, and R. W. Gunther, Skeletal maturity determination from hand radiograph by model based analysis, Proceedings SPIE, Vol.3979, 2000, pp [5] L. Vincent, P. Soille, Watersheds in digital spaces: an efficient algorithm based on immersion simulations, IEEE Trans. on Pattern Analysis and Machine Intelligent, Vol.13, No.6, 1991, pp [6] J. De Bock, P. De Smet, W. Philips, A fast sequential rainfalling watershed segmentation algorithm, Advance Concepts for Intelligent Vision System, 7 th international conference, Vol.3708, Springer 2005, pp [7] J. E. Cates, R. T. Whitaker and G. M. Jenes, Case study: an evaluation of user-assisted hierarchical watershed segmentation, Medical Image Analysis, Vol.9, No.6, 2005, pp [8] J. Sijbers, P. Scheunders, M. Verhoye, A. Van der Linden, D. Van Dyck, and E. Raman, Watershed-based segmentation of 3D MR data for volume quantization, Magnetic Resonance Imaging, Vol.15, No. 4, 1997, pp [9] A.P. Mangan, R.T. Whitaker, Partitioning 3D surface meshes using watershed segmentation, IEEE Trans. on Visualization and Computer Graphics, Vol.5, No.4, 1999, pp [10] K. Haris, S. Efstratiadis, N. Maglaveras and A. K. Katsaggelos, Hybrid image segmentation using watersheds and fast region merging, IEEE Trans. On Image Processing, Vol.7, No.12, 1998, pp [11] B. Peng, L. Zhang, D. Zhang, Automatic image segmentation by dynamic region merging, IEEE Trans. On Image Processing, No.99, 2011, pp [12] P. Perona, J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 12, No. 7,1990, pp [13] A. Jain, Fundamentals of Digital Image Processing, Englewood Cliffs, NJ, Prentice - Hall, [14] D. Giordano, R. Leonardi, F. Maiorana, G. Scarcifalo, Epiphysis and metaphysic extraction and classification by adaptive thresholding and DoG filtering for automated skeletal bone age analysis, Engineering in Medicine and Biology Society, Proceedings of the 29th Annual International Conference of the IEEE, 2007, pp [15] E. Pietka, A. Gertych, S. Pospiech, Cao Fei, H. K. Huang, V. Gilsanz, Computer assisted bone age assessment : image preprocessing and ROI extraction, IEEE Trans. On Medical Imaging, Vol. 20, No. 8, 2001, pp [16] C. Wang, Z.Y. Lie, Y. Ye, ROI boundary detection based on Geometric Active Contour Model in X-ray skeletal Image, Biomedical Engineering and Informatics, Proceedings of the International Conference on the IEEE, Vol. 2, 2008, pp [17] J.M. Lee, W.Y. Kim, Epiphyses extraction method using shape information for left hand radiograph, Convergence and Hybrid Information Technology, International Conference on the IEEE, 2008, pp [18] Qian Cui, Lin Wang, A method based on discrete tangent for curvature estimation of digital curve, Intelligent System (GCIS), Proceedings of the 1st WRI Global Congress on the IEEE, Vol. 4, 2009, pp [19] J. P. Braquelaire, A. Vialard, Euclidean paths : A new representation of boundary of discrete regions, Graphical Model and Image Processing, Vol. 61, No. 1, 1999, pp [20] E.M. Moreno, R. Cardenes, R.D. Luis- Garcia, M.M. Fernandez, C.A. Lopez, Image registration based on automatic detection of anatomical landmarks for bone age assessment, WSEAS Trans. On Computer, Vol.4, No.11, 2005, pp ISBN: