Medical image segmentation using improved chan-vese model

Abstract

Image segmentation plays a crucial role in many medical imaging applications. The use of the conventional Chan-Vese algorithm for medical image analysis is widespread because of its ability to produce a complete division of the image. However, its drawbacks include over-segmentation and sensitivity to false edges. In this paper, we present a novel algorithm that incorporates k-means clustering and improved Chan-Vese algorithm. The improved Chan-Vese algorithm is based on the similarity between each point and center point in the neighborhood. This algorithm can capture the details of the local region to realize the image segmentation in the gray-level heterogeneous area. The experimental results show that this method can segment the medical image with high accuracy, adaptability and more stable performance compared to the traditional Chan-Vese model.