Classification of Breast Tissues in Mammogram Images Using Ripley's K Function and Support Vector Machine

摘要

Female breast cancer is a major cause of death in western countries. Several computer techniques have been developed to aid radiologists to improve their performance in the detection and diagnosis of breast abnormalities. In Point Pattern Analysis, there is a statistic known as Ripley's K function that is frequently applied to Spatial Analysis in Ecology, like mapping specimens of plants. This paper proposes a new way in applying Ripley's K function in order to distinguish Mass and Non-Mass tissues from mammogram images. The features of each image are obtained through the calculate of that function. Then, the samples gotten are classified through a Support Vector Machine (SVM) as Mass or Non-Mass tissues. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. Another way of computing Ripley's K function, using concentric rings instead of a circle, is also examined. The best result achieved was 94.25% of accuracy, 94.59% of sensitvity and 94.00% of specificity.