用自产生和自组织神经网络对超声医学图像进行自动分割
AUTOMATIC SEGMENTATION OF MEDICAL ULTRASONIC IMAGE USING SELF-CREATING AND ORGANIZING NEURAL NETWORK
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摘要: 本文研究用自产生和自组织神经网络方法进行超声心脏图像的自动分割。这种无监督的聚类方法能够自动搜索最佳的网络输出节点数而获取图像中的目标数,从而完成对图像的自动分割。实验结果表明,与自组织特征映射方法相比,本文的方法具有许多重要的优点。Abstract: 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.
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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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