Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis.
Recent statistics show that breast cancer is a major cause of death among women in developed countries. Hence, finding an accurate and effective diagnostic method is very important. In this paper, we propose a high precision computer-aided diagnosis (CAD) system for sonography. We utilize a support vector machine (SVM) to classify breast tumors according to their texture information surrounding speckle pixels. We test our system with 250 pathologically-proven breast tumors including 140 benign and 110 malignant ones. Also we compare the diagnostic performances of three texture features, i.e., speckle-emphasis texture feature, nonspeckle-emphasis texture feature and conventional all pixels texture feature, applied to breast sonography using SVM. In our experiment, the accuracy of SVM with speckle information for classifying malignancies is 93.2% (233/250), the sensitivity is 95.45% (105/110), the specificity is 91.43% (128/140), the positive predictive value is 89.74% (105/117) and the negative predictive value is 96.24% (128/133). Based on the experimental results, speckle phenomenon is a useful tool to be used in computer-aided diagnosis; its performance is better than those of the other two features. Speckle phenomenon, which is considered as noise in sonography, can intrude into judgments of a physician using naked eyes but it is another story for application in a computer-aided diagnosis algorithm.
Chang
Ruey-Feng
RF
Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan.
Wu
Wen-Jie
WJ
Moon
Woo Kyung
WK
Chen
Dar-Ren
DR
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D012372:ROC Curve; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods*