A Multi Range Morphological Model on Dermoscopy Images with Edge Based Segmentation for Image Quality Enhancement for Skin Lesion Classification

Abstract

The most frequent form of cancer around the world is skin cancer. When compared to unaided visual inspection, Dermoscopy image processing enhances diagnostic accuracy for detecting malignant melanoma and other pigmented skin lesions. Medical professionals are very interested in computer-based methods that assist in diagnosis. For the most deadly skin diseases, such as melanoma, there has been a lot of research and development into the best ways to detect them. Skin cancer that originates in the pigment-producing cells known as melanocytes, melanoma, is the most severe form of the disease. Melanoma evolution might include changes in size, form, colour, irritation, and even skin disintegration. There is a need for a novel strategy to enhance the contrast and fine details of Dermoscopy Images. This study proposes a multi-range morphological approach to reducing the detrimental impact of low contrast on image quality. The rise in the number of skin cancer cases prompted the creation of these new technologies. The images must be precisely segmented in order to characterize skin lesions. That information can be used by a classifier or a dermatologist to classify a lesion more accurately, making it easier for them to do so. Segmentation issues might arise when images are obtained in an unsystematic and uncontrolled manner. Dermoscopy skin lesion photos can be used to aid in the computeraided diagnosis of melanoma by automatically selecting a lesion border. Segmenting skin lesions is a challenge because of the wide range of photography techniques used in Dermoscopy Images. The proposed model introduced a Multi Range Morphological Model on Dermoscopy Images with Edge based Segmentation (MRMM-DI-EbS) for image quality enhancement for lesion classification. The proposed model is compared with the traditional model and the results show that the proposed model performance is high in image quality enhancement.