Fast Image Segmentation Using FCM for Natural, SAR, and Texture Images

In this work, we implemented the Fuzzy C-Means (FCM) algorithm for fast segmentation of natural images, SAR images, and texture images. To achieve this, we employed feature mapping through both 1D and 2D histograms, which enhances segmentation accuracy by capturing spatial and intensity characteristics. The algorithm implementation involves computing histogram features as input vectors for FCM clustering, where the membership functions are optimized through iterative updates using weighted centroid calculations. Result screenshots clearly demonstrate the superior performance of our algorithm in image segmentation tasks. We conducted extensive experiments comparing our method with alternative algorithms, validating both effectiveness and advantages through quantitative metrics like segmentation accuracy and computational efficiency. The key implementation aspects include: - Preprocessing: Converting images to appropriate color/intensity spaces - Feature extraction: Generating 1D intensity histograms and 2D spatial-intensity histograms - FCM optimization: Implementing cluster center initialization and membership matrix updates - Post-processing: Applying morphological operations to refine segmentation boundaries Our approach not only achieves rapid image segmentation but also produces more accurate results, making it suitable for various image processing and computer vision applications including medical imaging, remote sensing, and pattern recognition. The method efficiently handles different image types through adaptive feature selection and optimized cluster validation techniques.