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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Recommended Citation
Salido, J. A., Ruiz, C., & Marcos, N. (2018). Artifact removal and lesion segmentation for melanoma detection in skin lesion images. 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, 2018-July, 392-396. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/1870
Disciplines
Computer Sciences
Keywords
Hair—Removal--Automation; Skin tests--Automation; Melanoma—Diagnosis
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