An Enhancement in K-means Algorithm for Automatic Ultrasound Image Segmentation

Breast malignancy is a relatively frequent disease that affects people all over the world. When interpreting the lesion component of medical images, inter- and intra-observer errors frequently happen, leading to considerable diversity in result interpretations. To combat this variability, computer-aided diagnosis (CAD) systems are essential. Automatic segmentation is an essential and critical step inCADsystems toward boundary detection, feature extraction, and classification. The aim of this study is to incorporate an Ant colony optimization (ACO) to initialize the cluster center and replace the Euclidean distance (ED) with the Manhattan distance (MD), in the traditional K-means algorithm to segment the BUS images with maximal area preservation. The Jaccard index (JI), Dice similarity (DS), and Area difference (AD) are the cluster validation measures used to compare the efficiency of the proposed method with other state-of-the-art methods. A total of 1293 BUS images are used in this study. According to the quantitative experimental findings, the suggested method can successfully segment the BUS images with an accuracy of 91.66%. Compared to existing methods, the proposed approach accomplishes segmentation more quickly and accurately.