Enhanced FCM Clustering for Medical Image Segmentation

This article examines the application of medical image segmentation, a process that utilizes computer algorithms and artificial intelligence techniques to partition medical images into distinct regions. In medical practice, this technology holds significant importance as healthcare professionals need to analyze and diagnose vast quantities of medical images. The implementation typically involves preprocessing techniques like noise reduction using Gaussian filters, followed by feature extraction through methods such as wavelet transforms. The core algorithm employs an enhanced Fuzzy C-Means (FCM) clustering approach that incorporates spatial constraints to improve segmentation accuracy. Key functions include membership matrix initialization, centroid calculation using weighted averages, and iterative optimization until convergence criteria are met. By leveraging this technology, physicians can more rapidly identify abnormalities, achieve more precise disease diagnoses, and develop improved treatment plans for patients. Therefore, the continued development and application of this technology carries substantial significance for the medical field.