Automatic Segmentation of Cancerous Tissues in Breast Ultrasound Images

Abstract

Segmentation of cancerous tissues in breast ultrasound images is a challenging task that can be achieved through various image processing and machine learning techniques. This paper presents an early automatic diagnostic method for breast cancer in ultrasound images and classifying the cancer as benign or malignant. Automatic images processing-based techniques were applied to process segment and classify cancerous tissues. The proposed method for early detection of breast tumor in ultrasound images typically involves three key steps: preprocessing, segmentation, and feature extraction and classification. The accuracy of proposed method was evaluated by comparing the segmented images with the gold standard or ground truth (GT), which was manually formed by an experienced radiologist. Areabased evaluation of segmentation was performed in which root mean squared error was 0.0435 and relative absolute error was 0.2085% for the total instances. The true positive result was 96% while false positive result was 3.11 %. The early detection of breast cancer in ultrasound images using the proposed image processing-based technique accurately detect breast cancer and classify cancer as benign or malignant. It also eliminates the drawbacks in the existing methods by introducing new features like shadows in the images using masking and adjusting contrast functions.