Development of Automatic Classification System for Mammographic Masses:

Saw-tooth Region Detection Method for Recognition of Mass Contour Shapes

Abstract

We have been developing a classification scheme for breast masses on mammograms as a part of a computer-aided diagnosis (CAD) system. In this study, the problem of classifying masses into benign and malignant using shape features is addressed. The type of the mass contour, such as circumscribed and microlobulated, is one of the features, and the recognition of this type is very important for classification. We attempted to improve recognition accuracy by using a new method to detect the saw-tooth region of a mass contour. The mass contours analyzed using the proposed method were drawn manually and extracted automatically by a method based on an active contour model to label them as convex segments. The shape features were calculated from the change of the slope of the tangent to the contour in the polar coordinate. A total of 160 masses (127 circumscribed and 33 microlobulated) were extracted from digitized mammograms for shape recognition. Moreover, a total of 202 masses (124 benign masses and 78 malignant) were used for benign/malignant classification. The corresponding accuracy using manually drawn contours was 88% (141/160). As a result of benign/malignant classification using this method, the classification rate was 84% (169/202) and the value was high compared to our conventional method based on fractal dimension. Automatically extracted contours achieved an accuracy of 76% (121/160) for shape recognition and an accuracy of 73% (148/202) for benign/malignant classification. The results demonstrate the feasibility of using the saw-tooth region detection method in shape recognition for classifying of benign and malignant masses on mammograms in a computer-aided diagnosis scheme.