https://scholars.lib.ntu.edu.tw/handle/123456789/302563
Title: | Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis | Authors: | RUEY-FENG CHANG Wen-Jie Wu Woo Kyung Moon Dar-Ren Chen |
Keywords: | Breast ultrasound; Computer-aided Diagnosis; Speckle; Support vector machine | Issue Date: | May-2003 | Journal Volume: | 29 | Journal Issue: | 5 | Start page/Pages: | 679--686 | Source: | Ultrasound in Medicine & Biology | Abstract: | 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. © 2003 World Federation for Ultrasound in Medicine & Biology. |
URI: | http://scholars.lib.ntu.edu.tw/handle/123456789/302563 https://www.scopus.com/inward/record.uri?eid=2-s2.0-0038670778&doi=10.1016%2fS0301-5629%2802%2900788-3&partnerID=40&md5=040f562241f9c5eda5642b16e0f78a89 |
ISSN: | 03015629 | DOI: | 10.1016/s0301-5629(02)00788-3 | SDG/Keyword: | Algorithms; 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 ratio |
Appears in Collections: | 資訊工程學系 |
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