RUEY-FENG CHANGWen-Jie WuWoo Kyung MoonDar-Ren Chen2018-09-102018-09-102003-0503015629http://scholars.lib.ntu.edu.tw/handle/123456789/302563https://www.scopus.com/inward/record.uri?eid=2-s2.0-0038670778&doi=10.1016%2fS0301-5629%2802%2900788-3&partnerID=40&md5=040f562241f9c5eda5642b16e0f78a89Recent 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. © 2003 World Federation for Ultrasound in Medicine & Biology.Breast ultrasound; Computer-aided Diagnosis; Speckle; Support vector machineAlgorithms; Computer aided diagnosis; Pathology; Speckle; Textures; Tumors; Support vector machines (SVM); Ultrasonics; article; breast tumor; cancer classification; cancer staging; computer aided design; computer simulation; correlation coefficient; device; diagnostic accuracy; diagnostic imaging; discriminant analysis; human; image analysis; intermethod comparison; prediction; priority journal; signal noise ratiojournal article10.1016/s0301-5629(02)00788-3127540672-s2.0-0038670778