Ahn, Euijoon, Bi, Lei, Jung, Youn Hyun, Kim, Jinman, Li, Changyang, Fulham, Michael, and Feng, David Dagan (2015) Automated saliency-based lesion segmentation in dermoscopic images. In: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 3009-3012. From: EMBC 2015: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 25-29 August 2015, Milan, Italy.

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Abstract

The segmentation of skin lesions in dermoscopic images is considered as one of the most important steps in computer-aided diagnosis (CAD) for automated melanoma diagnosis. Existing methods, however, have problems with over-segmentation and do not perform well when the contrast between the lesion and its surrounding skin is low. Hence, in this study, we propose a new automated saliency-based skin lesion segmentation (SSLS) that we designed to exploit the inherent properties of dermoscopic images, which have a focal central region and subtle contrast discrimination with the surrounding regions. The proposed method was evaluated on a public dataset of lesional dermoscopic images and was compared to established methods for lesion segmentation that included adaptive thresholding, Chan-based level set and seeded region growing. Our results show that SSLS outperformed the other methods in regard to accuracy and robustness, in particular, for difficult cases.

Item ID:	72042
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-4244-9271-8
Copyright Information:	© 2015 IEEE.
Date Deposited:	13 Dec 2023 02:10
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 60% 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460308 Pattern recognition @ 40%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 100%
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