CT Image Segmentation by using a FHNN Algorithm Based on Genetic Approach

摘要：

Traditional fuzzy Hopfield neural network (FHNN) is one of the excellent segmentation methods for CT image. Although FHNN has the capacity of searching values with high precision, it has obvious disadvantages, such as local minimum and slow convergence. In order to make up these shortcomings and find the right global minimum, a FHNN Algorithm based on genetic approach is proposed. Fine segmentation results have been obtained by the innovatory algorithm. Compared with corresponding segmentation results by means of the traditional FHNN method only, the experimental results of the innovative algorithm are better in CT image segmentation. The latter can segment the image more clearly, continuously, smoothly and has better capability in noise immunity. So the proposed approach possesses an important significance on computer aided diagnosis based on medical images segmentation.

关键词： Image segmentation Fuzzy Hopfield neural network genetic algorithms

作者: Jia Xin-Wang Ting Ting-Zhang

作者单位: Department of Computer Science and Technology Jilin University Changchun,China Ting Ting-Zhang Department of Biomedical Engineering Central South University Changsha,China

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）