A Pixel Interpolation Technique for Curved Hair Removal in Skin Images to Support Melanoma Detection

Melanoma Insitu is one of the most earliest perilous forms of skin cancer. These cancerous growths develop when unrepaired DNA damage to skin cells (most often caused by ultraviolet radiation) triggers mutations (genetic defects) that lead the skin cells to multiply rapidly and form malignant tumors. As there is no effective treatment for advanced melanoma, recognizing the lesion at an early stage is crucial for successful treatment. This lead to the development of several computer-aided methods to assist dermatologists. While diagnosing, hair occlusion caused algorithms to fail to identify the correct lesion in skin, and caused errors in the results. Removing hairs without altering the lesion in skin images is important to effectively apply detection algorithms. The challenge is to develop fast, precise and robust algorithms for the removal of hairs without altering the lesion in skin images. Hence, it leads to the techniques of image processing by identifying hair pixels within a binary image mask using the Pixel Interpolation Technique. The Pixel Interpolation Technique was adapted to find a quadratic curve which detects curved hairs in the image mask for removal and replacement through pixel interpolation. MATLAB [12] gives the platform to perform tests rapidly on both simulated and actual images for implementing this. Overall the quadratic Radon formula for Pixel Interpolation works nicely in being able to detect curves in the image and ignore the majority of image spots which are considered noise.