Diagnosis of Prostate Cancer and Texture Feature Extraction of Ultrasound Images Based on Wavelet Transform
Feng Huanqin · Hangtian yixue yu yixue gongcheng · 2009
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.