基于小波变换的肝脏CT图像分类  

Classification of liver CT images based on wavelet transform

作　　者：韩晓军[1] 赵宇[1] 杨国环 

机构地区：[1]天津工业大学电子与信息工程学院,天津300387

出　　处：《天津工业大学学报》2015年第1期59-63,共5页Journal of Tiangong University

基　　金：国家自然科学基金(61102150)

摘　　要：提出了一种基于小波变换的肝脏CT图像疾病的分类方法:首先提取小波和灰度共生矩阵纹理特征,其次结合马氏距离的可分性判据和遗传算法进行特征选择及优化,最后利用支持向量机将肝脏CT图像进行分类.讨论了2种小波以及特征提取方式对分类结果的影响,并通过软件仿真实现算法.实验表明:小波变换可对肝脏CT图像进行有效的分类.A liver CT image classification method based on wavelet transform was presented. Firstly, wavelet and gray levelco-occurrence matrix texture features were extracted. Secondly, Mahalonobis distance separability criterion andgenetic algorithms were cornbined for feature selection and optimization. Finally, the support vector machine wasused to classify the liver CT images. In this paper, features of two kind wavelets and extraction methods on theclassification were discussed. Also the algorithm was simulated by software. The experiments showed that the liverCT images can be classified effectively by wavelet transform.

关 键 词：肝脏CT图像 小波变换 灰度共生矩阵 支持向量机 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]