AUTOMATED SKIN LESION SEGMENTATION THROUGH HARMONIZING DERMOSCOPIC IMAGES WITH FUSION-BASED WHITE BALANCING

To resolve aberrations resulting from the dominating single color channel in the RGB plane, this work presents a novel approach for dividing skin lesions in dermoscopic pictures. The accuracy of melanoma diagnosis is greatly impacted by these abnormalities; hence a thorough approach is required. The suggested method combines undesirable hair removal, white balance, and picture segmentation based on unsupervised learning. Two different picture versions are produced by using the Colour Equalisation approach and Shades of Grey Method after starting with the iterative Dull Razor approach for efficient depilation. Dermoscopic image analysis is enhanced by integration using a multi-scale image fusion approach, which promises better benign lesion classification and melanoma detection accuracy. By combining two color-corrected versions, creating conclusive skin lesion delineation by k-means clustering, and leveraging inherent textural information acquired through the Gabor filter, the multi-scale image fusion approach further improves the process. A very detailed picture of the skin lesion is produced by combining the resultant three-segmented images, demonstrating the complexity of this cutting-edge dermoscopic image processing technique.