An Improved Hair Removal Method For Dermoscopic Images Towards Improving Skin Lesion Detection And Diagnosis

Abstract

Clinicians are continually improving their diagnostic abilities in the ever-changing medical field. A proper diagnosis is imperative for further health care delivery decisions. Skin cancers appear as lesions on any part of the skin, although most appearances are on body parts exposed to sunlight. Lesions may be either malignant or benign. The cells in malignant skin lesions can proliferate and spread to other tissues in other body parts. Malignant skin lesions are among the most lethal types of skin cancers, responsible for increased mortality rates. The diagnosis of skin diseases at the preliminary stages has been demonstrated to improve patients’ chances of abidance significantly, while the mortality rate for cases with late detection is on the rise. Computer-Aided Diagnostic systems play a very relevant role in thwarting the prevalence of skin cancer-related deaths by assisting in detecting skin lesions and assisting in diagnosis. Image analysis is enhanced by segmented images, highlighting previously undiscovered features of the original image. When detecting skin diseases from dermoscopic images, segmentation is used to separate the region of interest from the background. Lesions hidden behind elements such as hair, blue-white regions, globules, and an unusual pigment network can only be retrieved with accurate lesion border detection. Consequently, the most important phase is lesion segmentation in the early detection of melanoma. Detection of lesion borders in dermoscopy pictures is difficult because of the presence of noise such as skin lines, air bubbles, hairs and reflections. These are capable of causing significant segmentation mistakes when they are not adequately addressed. The existence of hairs is considered one of the most significant stumbling blocks for most algorithms when it comes to proper segmentation. The similarity of the hair color to the surrounding skin region is one of the main reasons for the difficulty. Improperly addressed hairs often tend to be classified as lesions, resulting in inaccurate segmentation and disease classification. In this thesis, selected hair removal and segmentation algorithms are evaluated and the effects of the hair removal method on the output of segmentation are also evaluated. In analyzing and evaluating a leading method for eliminating hairs in dermoscopic images, the dissertation presents an experimental implementation of a sequence of steps curated based on existing studies to improve segmentation results and justify the impact of these hair removal techniques on the output of image segmentation algorithms. This work uses Blackhat morphological filtering and Otsu’s method for thresholding and image inpainting to achieve segmentation with notable improvements recorded.