Hybrid Segmentation Approach for Different Medical Image Modalities

Abstract

The segmentation process requires separating the image region into sub-regions of similar properties. Each sub-region has a group of pixels having the same characteristics, such as texture or intensity. This paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization (PSO) and improved fast fuzzy C-means clustering (IFFCM) algorithms. An extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation techniques. The existing medical image segmentation techniques incorporate clustering, thresholding, graph-based, edge-based, active contour, region-based, and watershed algorithms. This paper extensively analyzes and summarizes the comparative investigation of these techniques. Finally, a prediction of the improvement involves the combination of these techniques is suggested. The obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.