A Feature Selection Method Base on GA for CBIR Mammography CAD
Feature selection is a very important step for almost all of the feature-based mammography computer-aided detection and diagnosis (CAD) system. The purpose of this study was to develop and evaluate a feature selection method for content-based image retrieval (CBIR) CAD system. After examine the problems in tradition genetic algorithm (GA), it is found that there usually are different feature subsets when running genetic algorithm (GA) for feature selection in different time, the reason of it is that the initial values for genes in GA are always generated randomly. Well then, which feature subset could be selected as the optimal one? Motivated by this, we proposed a method for feature selection which called F-GA (Frequency-GA). In the proposed method, GA was run m times for m different feature sub-sets. Then emergence frequency of each feature was counted. At last, those features which have highest frequency (i.e., ％p, p is a threshold) were selected to form the ultimate feature sub-set. To test and evaluated the performance of the proposed method, experiments on a public available data set were carried out. The experimental results demonstrated the effect of the proposed method.
Computer-aided detection and diagnosis content-based image retrieval(CBIR) mammography feature subset selection genetic algorithm performance evaluation
Yi Chen Yihua Lan Haozheng Ren
School of Science Hubei University of Technology Wuhan, China School of Computer Engineering Huaihai Institute of Technology Lianyungang, China
国际会议
南昌
英文
535-538
2012-08-26（万方平台首次上网日期，不代表论文的发表时间）