Artifact removal and lesion segmentation for melanoma detection in skin lesion images

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

MCCSIS 2018 - Multi Conference on Computer Science and Information Systems; Proceedings of the International Conferences on Interfaces and Human Computer Interaction 2018, Game and Entertainment Technologies 2018 and Computer Graphics, Visualization, Computer Vision and Image Processing 2018

Volume

2018-July

First Page

392

Last Page

396

Publication Date

1-1-2018

Abstract

Melanoma is a severe form of skin cancer characterized by the rapid multiplication of pigment-producing cells. There are new techniques for automated analysis of skin lesions for classification of melanoma using images from digital cameras and smart phones. A problem on analysis of these images are interesting because of the existence of artifacts and noise such as hair, veins, water residue, illuminations and light reflections. An important step in the diagnosis of melanoma is the removal of artifacts and reduction of noise that can inhibit the examination to accurately segment the skin lesion from the surrounding skin area. In this paper, a new method for extraction of skin lesion is implemented based on image enhancement and morphological operators. The experimental results show that artifact removal and lesion segmentation in skin lesion images can perform a true detection rate of 95.37% for melanoma skin lesion segmentation. © 2018.

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Disciplines

Computer Sciences

Keywords

Hair—Removal--Automation; Skin tests--Automation; Melanoma—Diagnosis

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