Segmentation of medical images using the KFCM method

Abstract

This study explores how the Kernel Fuzzy C-Means (KFCM) method can be used for medical image segmentation. The effectiveness of the KFCM method evaluated by comparing it with other well-known methods like fuzzy c-means and k-means. To ensure accurate assessment, proven evaluation metrics such as the Jaccard index and the Dice coefficient used for objective analysis. The experimental results demonstrate that the KFCM method is successful in accurately identifying different parts of the brain in various medical images. By combining the Fuzzy C-Means algorithm with kernel functions, the KFCM method improves the accuracy of clustering, resulting in more precise and dependable segmentation outcomes. This research contributes to the progress of medical image segmentation techniques, highlighting the promising capabilities of the KFCM method. Overall, the findings suggest that the KFCM method has the potential to be a valuable tool in medical image analysis, providing better results and advancing the field of medical diagnostics.