Comparison of Feature Extraction Algorithms for Mammography Images

摘要：

Mammography image classification is a very important research Held due to its domain of implementation. The aim of this paper is to compare feature extraction methods and to test them on a variety of classifiers. Five feature extraction methods were used: LBP, GLDM, GLRLM, Haralick and Gabor texture features. Three classification algorithms were used during the experiments, namely, support vector machines, k-nearest neighbor and c4.5 algorithm. The experiments were conducted on the MIAS database. The results show that GLDM is the most appropriate feature extraction method for images from this database.

关键词： component ntamography image classification haralick gabor Ibp gldm glrlm svm k-nn c4.5

作者: Ivan Kitanovski Blagojce Jankulovski Ivica Dimitrovski Suzana Loskovska

作者单位: Department of Computer Science and Engineering Faculty of Electrical Engineering and Information Technologies Skopje, Macedonia

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 902-906

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）