Segmentation of Carpal Bones Using Gradient Inverse Coefficient of Variation with Dynamic Programming Method
Segmentation of the carpal bones (CBs) especially for children above seven years old is a challenging task in computer vision mainly because of poor definitions of the bone contours and the occurrence of the partial overlapping of the bones. Although active contour methods are widely employed in image bone segmentation, they are sensitive to initialization and have limitation in segmenting overlapping objects. Thus, there is a need for a robust segmentation method for bone segmentation. This paper presents an automatic active boundary-based segmentation method, gradient inverse coefficient of variation, based on dynamic programming (DP-GICOV) method to segment carpal bones on radiographic images of children age 5 to 8 years old. A mapping procedure is designed based on a priori knowledge about the natural growth and the arrangement of carpal bones in human body. The accuracy of the DP-GICOV is compared qualitatively and quantitatively with the de-regularized level set (DRLS) and multi-scale gradient vector flow (MGVF) on a dataset of 20 images of carpal bones from University of Southern California. The presented method is capable to detect the bone boundaries fast and accurate. Results show that the DP-GICOV is highly accurate especially for overlapping bones, which is more than 85% in many cases, and it requires minimal user’s intervention. This method has produced a promised result in overcoming both issues faced by active contours method; initialization and overlapping objects.
W. W. Greulich and S. I. Pyle, Radiographic Atlas of Skeletal Development of Hand and Wrist, 2nd ed. ed. Stanford California USA: Stanford University Press, 1959.
J. M. Tanner and R. H. Whitehouse, Assessment of Skeletal Maturity and Predicion of Adult Height (TW2 Method). London, UK: Academic Press, 1975.
M. Niemeijer, "Automating skeletal age assessment," Master Institute of Information and Computing Sciences, Universiteit Utrecht, 2002.
G. W. Gross, J. M. Bonne, and D. M. Bishop, "Pediatric skeletal age: determination with neural networks," Radiology, vol. 195, pp. 689-695, 1995.
E. Pietka, A. Gertych, and K. Witko, "Informatics infrastructure of CAD system," Comput. Med. Imaging Graph., vol. 29, pp. 157-169, 2005.
C. W. Hsieh, T. L. Jong, and C. M. Tiu, "Bone age estimation based on phalanx information with fuzzy constrain of carpals," Med. Biol. Eng. Comput., vol. 45, pp. 283-295, 2007.
J. Liu, J. Qi, Z. liu, Q. Ning, and X. Luo, "Automatic bone age assessment based on intelligent algorithms and comparison with TW3 method," Comp. Med. Imaging and Graph., vol. 32, pp. 678-684, 2008.
A. Tristan-Vega and J. I. Arribas, "A radius and ulna TW3 bone age assessment system," IEEE Trans. Biomed. Eng., vol. 55, pp. 1463-1476, 2008.
M. Rucci, G. Coppini, I. Nicoletti, D. Chell, and G. Valli, "Automatic analysis of hand radiographs for the assessment of skeletal age: a subsymbolic approach," Comput Biomed Res, vol. 28, pp. 239-256, 1995.
D. S. O'Keefe, "Denoising of Carpal Bones for Computerised Assessment of Bone Age," Doctor of Philosophy, Dept of Electrical and Computer Engineering, Univ. of Canterbury, Christchurch, New Zealand, 2010.
H. Fatakwala, J. Xu, A. Basavanhally, G. Bhanot, S. Ganesan, M. Feldman, et al., "Expextation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR): Application to lymphocyte segmentation on breast cancer histopathology," IEEE Trans. Biomed. Eng., vol. 57, pp. 1676-1689, 2010.
J. Zhang, Z. Hu, G. Han, and X. He, "Segmentation of overlapping cells in cervical smears based on spatial relationship and overlapping translucency light transmission model," Procedia Tech, vol. 60, pp. 286–295, 2016.
H. Yang and N. Ahuja, "Automatic segmentation of granular objects in images: Combining local density clustering and gradient-barrier watershed," Pattern Recognit., vol. 47, pp. 2266–2279, 2014.
G. Mehul, P. Ankita, D. Namrata, G. Rahul, and S. Sheth, "Text-based image segmentation methodology," Procedia Tech, vol. 14, pp. 465-472, 2014.
J. Z. C. Park, J. Z. Huang, and Y. D. Ji, "Segmentation, inference, and classification of partially overlapping nanoparticles," IEEE Trans. Software Eng., vol. 35, pp. 669-681, 2012.
D. Giordano, C. Spampinato, G. Scarciofalo, and R. Leonardi, "An automatic system for skeletal bone age measurement by robust processing of carpal and epiphysial/metaphysial bones," IEEE Trans. Instrum. Meas., vol. 59, pp. 2539-2553, 2010.
P. Lin, F. Zhang, Y. Yang, and C. Zheng, "Carpal-bone feature extraction analysis in skeletal age assessment based on deformable model," J. of Comput. Sci. Technol., vol. 4, pp. 152-156, Oct. 2004.
K. Nandy, P. Gudla, and S. Lockett, "Automatic segmentation of cell nuclie in 2D using dynamic programming," in 2nd Workshop Microscopic Image Anal. Appl. Biol., Piscataway, NJ, 2007.
L. He, Z. Peng, B. Everding, X. Wang, C. Y. Han, K. L. Weiss, et al., "A comparative study of deformable contour methods on medical image segmentation," Image Visi. Comput., vol. 26, pp. 141-163, 2/1/ 2008.
F. Cloppet and A. Boucher, "Segmentation of overlapping/aggregating nuclei cells in biological images," in 19th Int. Conf. Pattern Recogn., Tampa, Florida, USA, 2008, pp. 1-4.
K. Ungru and X. Jiang, "Dynamic programming based segmentation in biomedical imaging," Comput. Struct. Biotechnol. J., vol. 15, pp. 255-264, 2017.
X. Qian, Y. Lin, Y. Zhao, J. Wang, J. Liu, and X. Zhuang, "Segmentation of myocardium from cardiac MR images using a novel dynamic programming based segmentation method," Medical Physics, vol. 42, pp. 1424-1435, 2015.
J. Bersvendsen, F. Orderud, R. J. Massey, K. Fosså, O. Gerard, S. Urheim, et al., "Automated Segmentation of the Right Ventricle in 3D Echocardiography: A Kalman Filter State Estimation Approach," IEEE Transactions on Medical Imaging, vol. 35, pp. 42-51, 2016.
C. Santiago, J. C. Nascimento, and J. S. Marques, "Fast segmentation of the left ventricle in cardiac MRI using dynamic programming," Comp. Methods and Prog. in Biomed., vol. 154, pp. 9-23, 2// 2018.
J. A. Rosado-Toro, T. Barr, J.-P. Galons, M. T. Marron, A. Stopeck, C. Thomson, et al., "Automated breast segmentation of fat and water MR images using dynamic programming," Acad. Radiol., vol. 22, pp. 139-148, 2015.
Q. Wang, E. Song, R. Jin, P. Han, X. Wang, Y. Zhou, et al., "Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques," Acad. Radiol., vol. 16, pp. 678-688, 2009/06/01/ 2009.
R. Bellman, "Dynamic Programming," Princeton Univerity Press, 1957.
A. Amini, T. Weymouth, and R. Jain, "Using dynamic programming for solving variational problems in vision " IEEE Trans. on PAMI, vol. 12, pp. 855-867, Sept 1990 1990.
G. Dong, N. Ray, and S. T. Acton, "Intravital leukocyte detection using the gradient inverse coefficient of variation," IEEE Trans. Med. Imag., vol. 24, pp. 910-924, 2005.
N. Ray, S. T. Acton, and Z. Hong, "Seeing through clutter: Snake computation with dynamic programming for particle segmentation," in 21st International Conference on Pattern Recognition (ICPR), Tsukuba Science City, Japan, 2012, pp. 801-804.
L. A. Zhang and J. Documet. (2008) Image Processing and Information Lab, University of Southern California. Homepage on Digital Hand Atlas Database System. [Online]. Available: http://ipilab.usc.edu/BAAweb/.
L. Chunming, X. Chenyang, G. Changfeng, and M. D. Fox, "Distance regularized level set evolution and its application to image segmentation," IEEE Trans. Image Process., vol. 19, pp. 3243-3254, 2010.
C. Xu and J. L. Prince, "Snakes, shapes, and gradient vector flow," IEEE Trans. Image Process., vol. 7, pp. 359-369, 1998.
T. Sorenson, "A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons," Roy Danish Ac. Sc. Lett., pp. 1-34, 1948.
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