Dermoscopy Image Segmentation in Melanoma Skin Cancer using Otsu Thresholding Method

Abstract

Melanoma is a skin cancer that originates from melanocytes, melanin-producing cells in the skin. It requires quite a long time to detect melanoma through a biopsy. By utilizing technology, the time required to obtain biopsy results in detection of melanoma can be shortened using image pattern recognition. Segmentation is a stage that affects the results in image analysis for pattern recognition in digital images because of the accuracy of a confident segment in an image analysis. Otsu thresholding is a segmentation method aims to find the threshold point that divides the grayscale image of histogram into two different areas automatically. In this study, segmentation was carried out on 15 dermoscopy of melanoma images that were subjected to grayscaling, histogram, segmentation with Otsu Thresholding, binarization, image negation, and testing. The test carried out using the Receiver Operating Character (ROC) method exhibited a mean sensitivity level of 70.3%, a mean specificity level of 95.53%, and a mean accuracy of 94.82%.