Exploring Edge-Based Segmentation Towards Automated Skin Lesion Diagnosis

Lau, Hui Keng and Chang, Jia Woei and Norhayati Daut, and Asni Tahir, and Erdah Samino, and Mohd Hanafi Bin Ahmad Hijazi, (2018) Exploring Edge-Based Segmentation Towards Automated Skin Lesion Diagnosis. Advanced Science Letters, 24 (2). pp. 1095-1099.

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Abstract

Automated medical diagnosis has many potentials and benefits to support healthcare. Therefore, there is growing number of research on this topic. There are many challenges before automated medical diagnosis is accepted by the healthcare industry and the public as a tool to facilitate healthcare professionals. In this paper, initial work on exploring edge-based segmentation algorithms to identify areas on an image that form the skin lesion is presented. Four edge-segmentation operators namely Canny, Prewitt, Sobel, and Roberts were tested using images from online image database. Experiments show results with mixed accuracy depending on the quality of image as well as the pattern of the skin lesions.

Item Type: Article
Uncontrolled Keywords: Automated Medical Diagnosis, Edge-Based Segmentation, Skin Lesions
Subjects: R Medicine > RZ Other systems of medicine
T Technology > T Technology (General)
Divisions: FACULTY > Faculty of Computing and Informatics
Depositing User: MDM SITI AZIZAH IDRIS
Date Deposited: 29 Oct 2019 23:41
Last Modified: 29 Oct 2019 23:41
URI: http://eprints.ums.edu.my/id/eprint/23879

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