Brain image segmentation using K mean segmentation and fuzzy C-means (FCM) algorithm to improve efficiency of tumor detection

Brain tumor segmentation plays a vital role in medical image analysis, aiding in accurate diagnosis and treatment planning. This research article proposes a novel approach for brain tumor segmentation utilizing the K-Means clustering algorithm and Fuzzy C-Means (FCM) clustering technique. The K-means segmentation is initially used to cluster the brain image into distinct regions based on intensity values. Subsequently, the FCM algorithm is applied to further refine the segmentation results by considering the fuzzy memberships of pixels within each cluster. The combination of K-means segmentation and FCM algorithm improved the tumor detection accuracy and also reduced computational complexity compared to traditional methods. The objective is to enhance the accuracy and efficiency of tumor segmentation in magnetic resonance imaging (MRI) scans.