Wang Tianfu, Li Deyu, Zheng Changqiong, Zheng Yi, Ran Junguo. AUTOMATIC SEGMENTATION OF MEDICAL ULTRASONIC IMAGE USING SELF-CREATING AND ORGANIZING NEURAL NETWORK[J]. Journal of Electronics & Information Technology, 1999, 21(1): 124-127.
Citation:
Wang Tianfu, Li Deyu, Zheng Changqiong, Zheng Yi, Ran Junguo. AUTOMATIC SEGMENTATION OF MEDICAL ULTRASONIC IMAGE USING SELF-CREATING AND ORGANIZING NEURAL NETWORK[J]. Journal of Electronics & Information Technology, 1999, 21(1): 124-127.
Wang Tianfu, Li Deyu, Zheng Changqiong, Zheng Yi, Ran Junguo. AUTOMATIC SEGMENTATION OF MEDICAL ULTRASONIC IMAGE USING SELF-CREATING AND ORGANIZING NEURAL NETWORK[J]. Journal of Electronics & Information Technology, 1999, 21(1): 124-127.
Citation:
Wang Tianfu, Li Deyu, Zheng Changqiong, Zheng Yi, Ran Junguo. AUTOMATIC SEGMENTATION OF MEDICAL ULTRASONIC IMAGE USING SELF-CREATING AND ORGANIZING NEURAL NETWORK[J]. Journal of Electronics & Information Technology, 1999, 21(1): 124-127.
The automatic segmentation of ultrasonic heart image using self-creating and organizing neural network has been studied. This kind of unsupervised clustering method can search for the optimal number of output nodes automatically to get the number of textures in the image, and finish the automatic segmentation. Experimental results show that this method has significant benefits over self-organizing neural network method.
Manjunath B S, Chellappa R. Unsupervised texture segmentation using Markov random models. IEEE Trans. on PAMI, 1991, PAMI-13(5): 478-482.[2]Jain A K, Farrokhnia F. Unsupervised texture segmentation using Gabor filters[J].Pattern Recognition.1991, 24(12):1167-1186[3]Bouman C, Liu B. Multiple resolution segmentation of textured images. IEEE Trans. on PAMI, 1991, PAMI-13(2): 99-113.[4]Kohonen T. Self-Organization and Associative Memory. 2nd ed., New York: Springer-Verlag,[5]Ch.5: 119-157.[6]Choi D I, Park S H. Self-creating and organizing neural network. IEEE Trans. on NN, 1994,[7]NN-5(4): 561-575.
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