Image Segmentation Using Fuzzy C-Mean

Image segmentation plays a crucial role in image processing and computer vision applications. This paper presents an efficient segmentation approach using the Fuzzy C-Means (FCM) clustering algorithm. Unlike hard clustering methods, FCM allows each pixel to belong to multiple clusters with different membership degrees, leading to smoother and more accurate segmentation. The proposed method is applied on sample images, and the results demonstrate improved region separation and robustness against noise. This study highlights the effectiveness of FCM for medical imaging, pattern recognition, and object detection tasks.