Feature extraction for classifying lesion´s shape of breast ultrasound images

The reading of ultrasound results is subjective, depending on the radiologist. Therefore, physicians need a tool as second opinion to make decision about the diagnosis of breast cancer malignancies depending on various parameters including shape parameter. To determine this parameter, a method to classify the lession´s shape type of breast ultrasound image with image processing technique is proposed. In this research, two extraction feature methods, namely zernike moment and invariant moment are compared. This research also compares two methods of classifier, namely support vector machine (SVM) and multilayer perceptron (MLP). The first step is to determine region of interest (ROI) from lesion image, then the image will be filtered to reduce speckle noise. The adaptive median filter is applied to filter the input image followed by segmentation based on chan-vese active contour. Feature extraction is conducted by using Zernike moments and invariant moment, followed by classification process by using support vector machine (SVM) and multilayer perceptron (MLP). From the 45 images, the proposed method achieves 80% for classification.