Classification of Breast Tumour in Contourlet Transform Domain

Classification of Benign and Malignant breast cancer tumours by B-Mode ultrasound image analysis is a challenging problem. In this study, first suitable raw radio frequency (RF) frame is selected based on strain and velocity imaging. The consequent B-Mode ultrasound (US) image is binarized and subsequently subjected to contourlet transform. In the proposed method only six contourlet sub-band coefficients are considered from the binary image to reduce the computation time duration and then texture and statistical features are extracted from the contourlet coefficient image. Finally, the lesion is classified as benign or malignant using three classifiers such as artificial neural network (ANN), K-Nearest Neighbours (KNN) and Support Vector Machine (SVM). It is shown that the proposed method yields classification accuracies of 95%, 92.5% and 90% over a competent computational time by using ANN, KNN and SVM classifiers respectively.