title: Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors.
abstract: Ultrasound (US) is a useful diagnostic tool to distinguish benign from malignant masses of the breast. It is a very convenient and safe diagnostic method. However, there is a considerable overlap benignancy and malignancy in ultrasonic images and interpretation is subjective. A high performance breast tumors computer-aided diagnosis (CAD) system can provide an accurate and reliable diagnostic second opinion for physicians to distinguish benign breast lesions from malignant ones. The potential of sonographic texture analysis to improve breast tumor classifications has been demonstrated. However, the texture analysis is system-dependent. The disadvantages of these systems which use texture analysis to classify tumors are they usually perform well only in one specific ultrasound system. While Morphological based US diagnosis of breast tumor will take the advantage of nearly independent to either the setting of US system and different US machines. In this study, the tumors are segmented using the newly developed level set method at first and then six morphologic features are used to distinguish the benign and malignant cases. The support vector machine (SVM) is used to classify the tumors. There are 210 ultrasonic images of pathologically proven benign breast tumors from 120 patients and carcinomas from 90 patients in the ultrasonic image database. The database contains only one image from each patient. The ultrasonic images are captured at the largest diameter of the tumor. The images are collected consecutively from August 1, 1999 to May 31, 2000; the patients' ages ranged from 18 to 64 years. Sonography is performed using an ATL HDI 3000 system with a L10-5 small part transducer. In the experiment, the accuracy of SVM with shape information for classifying malignancies is 90.95% (191/210), the sensitivity is 88.89% (80/90), the specificity is 92.5% (111/120), the positive predictive value is 89.89% (80/89), and the negative predictive value is 91.74% (111/121).
authors.affiliation: Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan.
authors.forename: Ruey-Feng
authors.initials: RF
authors.lastname: Chang
authors.forename: Wen-Jie
authors.initials: WJ
authors.lastname: Wu
authors.forename: Woo Kyung
authors.initials: WK
authors.lastname: Moon
authors.forename: Dar-Ren
authors.initials: DR
authors.lastname: Chen
mesh_terms: D000293:Adolescent; D000328:Adult; D001331:Automation; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D003937:Diagnosis, Differential; D005188:False Negative Reactions; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008875:Middle Aged; D015588:Observer Variation; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*