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Recognition of kidney glomerulus by dynamic programming matching method
September 1988 (vol. 10 no. 5)
pp. 731,732,733,734,735,736,737
Dynamic programming was applied to locate the glomeruli in microscopic images of kidney tissue section. The glomeruli were modeled by a polygon whose sides could be varied within a given range of lengths. The objects were located by determining the best match of the model according to a so-called optimum criterion in which all possible shapes were evaluated at all possible positions in the input image. The best model was selected according to the maximum average gray level. To increase the probability of obtaining a closed contour, a distance criterion was added and the maximum gray-level requirement was relaxed somewhat. The optimum criterion was modified to include a directionality constraint in which the difference in angle between model segments and the edge values in the image was minimized, thereby increasing the performance of the method. A hierarchical multiresolution strategy was used to reduce calculation time. The cyclical property of a contour is also taken into account.<>

[1] D. H. Ballard and C. M. Brown,Computer Vision. Englewood Cliffs, NJ: Prentice-Hall, 1982.
[2] R. Bellman,Dynamic Programming. Princeton, NJ: Princeton University Press, 1957.
[3] M. A. Fischler and R. A. Elschlager, "The representation and matching of pictorial structures,"IEEE Trans. Comput., vol. C-22, no. 1, pp. 67-92, Jan. 1973.
[4] D. H. Hubel and T. N. Wiesel, "Functional architecture of macaque monkey visual cortex,"Proc. Roy. Soc. London B, vol. 198, pp. 1- 59, 1977.
[5] V. A. Kovalevsky, "Sequential optimization in pattern recognition and pattern description," inProc. IFIP Congr. 68, pp. 1603-1607.
[6] V. A. Kovalevsky,Image Pattern Recognition. New York: Springer-Verlag, 1980.
[7] M. D. Levine and D. Ting, "Intermediate level picture interpretation using complete two-dimensional models,"Comput. Graphics Image Processing, vol. 16, pp. 185-209, 1981.
[8] A. Martelli and U. Montanari, "Optimal smoothing in picture processing: An application to fingerprints," inProc. IFIP Congr. 71, pp. 173-178.
[9] U. Montanari, "On the optimal detection of curves in noisy pictures,"Comm. ACM, vol. 14, pp. 335-345, 1971.
[10] H. Ney, "A dynamic programming as a technique for pattern recognition," inProc. 6th Int. Conf. Pattern Recog., 1982, pp. 1119- 1125.
[11] H. Sakoe, "Handwritten character recognition based on rubber string matching," IECE Japan, Tech. Rep. PRL74-20, 1974 (in Japanese).
[12] H. Yamada, "Contour DP matching method and its application to bandprinted Chinese character recognition," inProc. 7th Int. Conf. Pattern Recog., 1984, pp. 389-392.
[13] H. Yamada, K. Yamamoto, and M. Matsuura, "Recognition of ultrasonic human kidney organ images by the two-dimensional DP matching method,"Trans. IECE Japan, vol. J68-D, no. 10, pp. 1649-1656, 1985 (in Japanese).
[14] H. Yamada and T. Kasvand, "DP matching method for recognition of occluded, reflective and transparent objects with unconstrained background and illumination," inProc. 8th Int. Conf. Pattern Recog., Paris, Oct. 1986, pp. 95-98.

Index Terms:
medical diagnostic computing,computerised pattern recognition,dynamic programming,kidney,hierarchical multiresolution,image matching,computerised pattern recognition,edge detection,biomedical image analysis,shape recognition,kidney glomerulus,dynamic programming,polygon,gray-level,model segments,Dynamic programming,Navigation,Mobile robots,Shape,Error analysis,Computer errors,Robotics and automation,Image analysis,Data mining,Biomembranes
"Recognition of kidney glomerulus by dynamic programming matching method," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 5, pp. 731,732,733,734,735,736,737, Sept. 1988, doi:10.1109/34.6784
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