基于小波变换的超声图像纹理特征提取及前列腺癌诊断  

Diagnosis of Prostate Cancer and Texture Feature Extraction of Ultrasound Images Based on Wavelet Transform

作　　者：杨振森[1] 李传富[2] 周康源[1] 刘伟[3] 冯焕清[3] 

机构地区：[1]中国科学技术大学电子工程与信息科学系,安徽合肥230027 [2]安徽中医学院第一附属医院影像中心,安徽合肥230031 [3]中国科学技术大学电子科学与技术系

出　　处：《航天医学与医学工程》2009年第4期281-285,共5页Space Medicine & Medical Engineering

基　　金：安徽省教委自然科学基金重点研究项目(2006KJ097A)

摘　　要：目的根据小波变换原理,研究了前列腺直肠超声图像中纹理特征的提取方法,并应用于前列腺癌的早期诊断。方法本文提取出前列腺直肠超声图像中目标区域的小波变换纹理特征和边界频率特征,通过主分量分析方法(principal components analysis,PCA)对提取出的纹理特征进行选择,得到一个最优的特征子集。然后分别应用K均值聚类、支持向量机(support vector machine,SVM)算法和AdaBoost(a-daptive boosting)算法来对所提取出的病变区域纹理特征进行分类。结果对比实验结果表明,本文所提取的特征比单纯的使用灰度级差矢量(gray level difference vector,GLDV)具有更好的区分良恶性图像的能力,AdaBoost算法和SVM算法都能够有效地识别病变区域,识别正确率达到94.12%和93.46%。结论使用本文算法可以为医生诊断提供有用的辅助信息,并提高诊断效率。Objective To study the texture feature extraction of prostate ultrasound images based on the wavelet transform for the early diagnosis of prostate cancer. Methods This paper extracted the wavelet texture features and edge-frequency features from pathological regions in transrectal ultrasound images,then the reduced optimal feature set was selected by principal components analysis（PCA） algorithm,and the classification was done by K-means,support vector machine（SVM） and AdaBoost algorithm individually. Results We compared the texture features with Mohamed＇s,the experiment results showed that the extracted features had the better ability to differentiate the benign or malignant images than the mere gray level difference vector （GLDV）. AdaBoost and SVM could differentiate the pathology regions efficiently and gave the identify rate of 94.12%,93.46% respectively. Conclusion The proposed algorithm can supply useful information to the doctors for the clinical diagnosis and the diagnosis efficiency is enhanced.

关 键 词：前列腺癌 小波纹理特征 SVM算法 ADABOOST算法 计算机辅助诊断 

分 类 号：R445.1[医药卫生—影像医学与核医学]