UNVEILING SKIN DISEASE: ADVANCEMENTS IN CONTOUR DETECTION TECHNIQUES FOR ENHANCED DETECTION AND DIAGNOSIS

Abstract

Skin lesion detection is a critical task in dermatology for the early diagnosis and treatment of skin cancer. This paper presents a novel approach combining median filter preprocessing and active contour modeling to enhance the accuracy and efficiency of skin lesion detection. The proposed method consists of two primary stages: preprocessing using a median filter to reduce noise and improve image quality, followed by lesion segmentation utilizing active contour models. In the preprocessing stage, the median filter is applied to the dermoscopic images to suppress noise while preserving the edges of lesions. This step ensures that the subsequent segmentation process operates on high-quality images, which is crucial for precise boundary detection. The second stage employs active contour models, also known as snakes, which iteratively evolve to fit the contours of the skin lesion. The active contour model is initialized around the region of interest and driven by internal and external forces derived from the image data. These forces guide the contour towards the lesion boundaries, enabling accurate segmentation even in the presence of irregular shapes and varying contrast levels. Experimental results demonstrate that the combination of median filtering and active contour models significantly improves the detection and segmentation of skin lesions compared to traditional methods. The proposed method exhibits robustness against noise, enhances edge preservation, and accurately delineates complex lesion boundaries. This approach holds promise for integration into automated diagnostic systems, potentially aiding dermatologists in the early and accurate detection of skin cancer.