Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines

Authors:
Yu-Len Huang;Kao-Lun Wang;Dar-Ren Chen
Affiliations:
Tunghai University, Department of Computer Science and Information Engineering, P.O. Box 5-809, Taichung, Taiwan 407, Republic of China;Chung Shan Medical University Hospital, Department of Medical Imaging and Technology, P.O. Box 5-809, Taichung, Taiwan, Republic of China;Changhua Christian Hospital, Department of General Surgery, P.O. Box 5-809, Changhua, Taiwan, Republic of China
Venue:
Neural Computing and Applications
Year:
2006

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Abstract

This study presents a computer-aided diagnosis (CAD) system with textural features for classifying benign and malignant breast tumors on medical ultrasound systems. A series of pathologically proven breast tumors were evaluated using the support vector machine (SVM) in the differential diagnosis of breast tumors. The proposed CAD system utilized facile textural features, i.e., block difference of inverse probabilities, block variation of local correlation coefficients and auto-covariance matrix, to identify breast tumor. An SVM classifier using the textual features classified the tumor as benign or malignant. The proposed system identifies breast tumors with a comparatively high accuracy. This can help inexperienced physicians avoid misdiagnosis. The main advantage of the proposed system is that the training and diagnosis procedure of SVM are faster and more stable than that of multilayer perception neural networks. With the expansion of the database, new cases can easily be gathered and used as references. This study dramatically reduces the training and diagnosis time. The SVM is a reliable choice for the proposed CAD system because it is fast and excellent in ultrasound image classification.