Fuzzy C-means Clustering-based Multilayer Perceptron Neural Network for Liver CT Images Automatic Segmentation

摘要：

A new liver segmentation algorithm is proposed. First, the threshold method was used to remove the ribs and spines in the initial image, and the fuzzy C-means clustering algorithm and morphological reconstruction filtering were used to segment the initial liver CT image. Then the multilayer perceptron neural network was trained by the segmentation result of initial image with the back-propagation algorithm. The adjacent slice CT image was segmented with the trained multilayer perceptron neural network. Last, morphological reconstruction filtering was used to smooth the contour of the liver edge. The experimental results show that the proposed algorithm can effectively segment the livers from CT images, despite the gray level similarity of adjacent organs and different gray level of tumors in the liver.

关键词： Liver Segmentation Fuzzy C-means Algorithm Multilayer Perceptron Neural Network Morphological Reconstruction Filtering

作者: Yuqian Zhao Yunlong Zan Xiaofang Wang Guiyuan Li

作者单位: School of Info-Physics & Geomatics Engineering, Central South University, Changsha 410083, China Can School of Info-Physics & Geomatics Engineering, Central South University, Changsha 410083, China Cancer Research Institute, Central South University, Changsha 410078, China

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 3423-3427

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）