This paper presents a new strategy for the segmentation of trabecular bone image. This kind of image is acquired with microcomputed tomography (micro-CT) to assess bone microarchitecture based chiefly on bone mineral density (BMD) measurements to improve fracture risk prediction. Disease osteoporosis can be predicted from features of CT image where a bone region may consist of several disjoint pieces. It relies on a multiresolution representation of the image by the wavelet transform to compute the multiscale morphological gradient. The coefficients of detail found at the different scales are used to determine the markers and homogeneous regions that are extracted with the watershed algorithm. The method reduces the tendency of the watershed algorithm to oversegment and results in closed homogeneous regions. The performance of the proposed segmentation scheme is presented via experimental results obtained with a broad series of images. 1. Introduction Osteoporosis is a metabolic bone disease [1]. The disease is defined by low bone mass and microarchitectural deterioration of bone tissues leading to enhanced bone fragility. Clinically, osteoporosis is associated with an increased risk of fractures of the vertebral bone. The exact clinical estimation of bone strength and fracture risk is important for the treatment of bone diseases such as osteoporosis. It designates a major health problem in industrialized countries. This is a silent disease without any symptoms. After the age of 50, the number of osteoporotic people clearly raises [2]. A hip fracture is costly for a system of health care. Vertebral fractures are associated with a higher risk of mortality and also having another fracture in the first months following a fracture. Segmentation of bone structures from computed tomography (CT) images (shown in Figure 1) is to extract osseous tissue on the surface of bones (cortical bone, the trabecular network) from the interior region of bones (cancellous bone, space medullar). Figure 1: Trabecular bone image. The trabecular network detection is a very important task to help diagnose diseases such as trabecular bone loss in osteoporotic patients or in animal models of osteoporosis [1–3]. The low contrast images of the trabecular bone acquired with the sky-scan X-ray microcomputed tomography make their analysis a challenging task. Additionally, throughout acquisition, it is required to establish a high number of parameters that result in the presence of noise, nonhomogeneous illumination, and fuzzy contours. Such segmentations can be very challenging to
References
[1]
M. Audran, D. Chappard, E. Legrand, H. Libouban, and M. F. Baslé, “Bone microarchitecture and bone fragility in men: DXA and histomorphometry in humans and in the orchidectomized rat model,” Calcified Tissue International, vol. 69, no. 4, pp. 214–217, 2001.
[2]
D. Chappard, M.-F. Baslé, E. Legrand, and M. Audran, “Trabecular bone microarchitecture: a review,” Morphologie, vol. 92, no. 299, pp. 162–170, 2008.
[3]
R. Filmon, N. Retailleau-Gaborit, F. Grizon et al., “Non-connected versus interconnected macroporosity in poly(2-hydroxyethyl methacrylate) polymers. An X-ray microtomographic and histomorphometric study,” Journal of Biomaterials Science, Polymer Edition, vol. 13, no. 10, pp. 1105–1117, 2002.
[4]
L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583–598, 1991.
[5]
J. B. T. Roerdink and A. Meijster, “The watershed transform: definitions, algorithms and parallelization strategies,” Fundamenta Informaticae, vol. 41, no. 1-2, pp. 187–228, 2001.
[6]
F. Meyer, “Flooding and Segmentation,” in Mathematical Morphology and Its Applications to Image and Signal Processing, pp. 189–198, Springer, 2000.
[7]
J. M. Gauch, “Image segmentation and analysis via multiscale gradient watershed hierarchies,” IEEE Transactions on Image Processing, vol. 8, no. 1, pp. 69–79, 1999.
[8]
S. Mukhopadhyay and B. Chanda, “Multiscale morphological segmentation of gray-scale images,” IEEE Transactions on Image Processing, vol. 12, no. 5, pp. 533–549, 2003.
[9]
B. Kim, J. Shim, and D. Park, “Fast image segmentation based on multi-resolution analysis and wavelets,” Pattern Recognition Letters, vol. 24, no. 16, pp. 2995–3006, 2003.
[10]
C. R. Jung, “Combining wavelets and watersheds for robust multiscale image segmentation,” Image and Vision Computing, vol. 25, no. 1, pp. 24–33, 2007.
[11]
R. J. O'Callaghan and D. R. Bull, “Combined morphological-spectral unsupervised image segmentation,” IEEE Transactions on Image Processing, vol. 14, no. 1, pp. 49–62, 2005.
[12]
F. Meyer, “Levelings and morphological segmentation,” in Proceedings of the International Symposium on Computer Graphics, Image Processing, and Vision (SIBGRAPI '98), pp. 28–35, Rio de Janeiro, Brazil, 1998.
[13]
J. Serra and P. Soille, Mathematical. Morphology and Its Applications to Image Processing, Kluwer Academic, Dodrecht, The Netherlands, 1994.
[14]
E. Mor and M. Aladjem, “Boundary refinements for wavelet-domain multiscale texture segmentation,” Image and Vision Computing, vol. 23, no. 13, pp. 1150–1158, 2005.
[15]
W. Abid, K. Taouil, and M. S. Bouhlel, “Bone histological slice image segmentation based on watershed,” in Proceedings of the 2007 International Conference: Sciences of Electronic, Technologies of Information and Telecommunications, Hammamet, Tunisia, March 2007.
[16]
C. R. Jung and J. Scharcanski, “Robust watershed segmentation using wavelets,” Image and Vision Computing, vol. 23, no. 7, pp. 661–669, 2005.
[17]
J. Kim and H. Kim, “Multiresolution-based watersheds for efficient image segmentation,” Pattern Recognition Letters, vol. 24, no. 1–3, pp. 473–488, 2003.
[18]
J. B. Kim and H. J. Kim, “A wavelet-based watershed image segmentation for VOP generation,” in Proceedings of the IEEE Internatinal Conference on Pattern Recognition, vol. 3, pp. 505–508, Québec City, Canada, 2002.
[19]
I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Communications on Pure and Applied Mathematics, vol. 41, no. 7, pp. 909–996, 1988.
[20]
G. STRANG and T. NGUYEN, Wavelets and Filter Banks, Wellesley-Cambridge Press, 1996.
[21]
Y.-K. Sun, Wavelet Analysis and Application, vol. 3(1), China Machine Press, 2005.
[22]
N. COUDRAY, J. L. BUESSLER, H. KIHL, and J. P. URBAN, “TEM images of membranes: a multiresolution edge-detection approach for watershed segmentation,” in Proceeding of the 5th Workshop on Physics in Signal and Image Processing (PSIP '07), Mulhouse, France, 2007.
[23]
I. Vanhamel, I. Pratikakis, and H. Sahli, “Multiscale gradient watersheds of color images,” IEEE Transactions on Image Processing, vol. 12, no. 6, pp. 617–626, 2003.
[24]
R. C. Gonzales and R. E. Woods, Digital Image Processing, 2nd edition, 2002.
[25]
W. Abid, K. Taouil, and M. S. Bouhlel, “Bone histological slice image segmentation based on watershed,” in International Conference: Sciences of Electronic, Technologies of Information and Telecommunications, Tunis, Tunisia, March 2007.
