Enhanced Skin Disease Image Analysis Using Hybrid CLAHE-Median Filter and Salient K-Means Cluster

Skin diseases are prevalent, and accurate diagnosis is crucial for effective treatment. However, noise in medical images can hinder accurate diagnosis. This research proposes a comprehensive approach for automatic noise filtering and segmentation of skin diseases using a hybrid Contrast Limited Adaptive Histogram Equalization (CLAHE) and median filter algorithm with salient K-means cluster. The approach involves preprocessing skin disease images with a median filter to remove noise and enhancing contrast using the CLAHE method. The salient algorithm is employed for initial segmentation of the skin lesion, followed by further segmentation into different regions using the K-means clustering algorithm. The proposed approach is evaluated and compared with state-of-the-art algorithms such as Otsu thresholding, K-means clustering, and Fuzzy C-means clustering. Experimental results demonstrate the superiority of our approach in terms of accuracy and robustness. This research contributes to automatic noise filtering and segmentation of skin diseases, enabling faster and more accurate diagnosis and treatment.