Ultrasound breast tumor image computer-aided diagnosis with texture and morphological features.
OBJECTIVE
Computer-aided diagnosis (CAD) systems based on shape analysis have been proved to be highly accurate in evaluating breast tumors. However, it takes considerable time to train the classifier and diagnose breast tumors, because extracting morphologic features require a lot of computation. Hence, to develop a highly accurate and quick CAD system, we combined the texture and morphologic features of ultrasound breast tumor imaging to evaluate breast tumors in this study.


METHODS
This study evaluated 210 ultrasound breast tumor images, including 120 benign tumors and 90 malignant tumors. The breast tumors were segmented automatically by the level set method. The autocovariance texture features and solidity morphologic feature were extracted, and a support vector machine was used to identify the tumor as benign or malignant.


RESULTS
The accuracy of the proposed diagnostic system for classifying breast tumors was 92.86%, the sensitivity was 94.44%, the specificity was 91.67%, the positive predictive value was 89.47%, and the negative predictive value was 95.65%. In addition, the proposed system reduced the training time compared to systems based only on the morphologic analysis.


CONCLUSIONS
The CAD system based on texture and morphologic analysis can differentiate benign from malignant breast tumors with high accuracy and short training time. It is therefore clinically useful to reduce the number of biopsies of benign lesions and offer a second reading to assist inexperienced physicians in avoiding misdiagnosis.
Wu
Wen-Jie
WJ
Department of Information Management, Chang Gung University, Tao-Yuan, Taiwan 333, ROC. wjwu@mail.cgu.edu.tw
Moon
Woo Kyung
WK
D000293:Adolescent; D000328:Adult; D016880:Anisotropy; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008875:Middle Aged; D011237:Predictive Value of Tests; D012372:ROC Curve; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary