An Adaptive Approach for Skin Lesion Segmentation in Dermoscopy Images Using a Multiscale Local Normalization

Abstract

Skin cancer is one of the most common malignancies in humans. Early detection of suspicious skin signs is critical to prevent this kind of malignancy, and various disciplines can play a crucial role in its detection. The lesion border is especially relevant for diagnosis, and provides information on the shape of the lesion, growth path, and growth rate. Digital image processing methods can be used to perform automatic lesion border detection; nonetheless, the presence of artifacts may induce artificial borders, thereby jeopardizing the efficiency of automatic detection algorithms. Artifact removal is a necessary pre-processing step to improve the accuracy quality of the border identification. In this work, we present a method to identify and remove artifacts in dermoscopic images. This pre-processing step enhances the output of the segmentation of the lesion. This process is based on several applications of the Local Normalization, which is a method that increases the local contrast between local pixels, improving the overall quality of the image, especially with non-uniform illumination. The process is scale sensitive and uses a multi-scale approach adaptable to every shape and size of skin lesions.