Bi, Lei, Kim, Jinman, Ahn, Euijoon, Feng, Dagan, and Fulham, Michael (2017) Semi-automatic skin lesion segmentation via fully convolutional networks. In: Proceedings of the IEEE 14th International Symposium on Biomedical Imaging. pp. 561-564. From: ISBI 2017: IEEE 14th International Symposium on Biomedical Imaging, 18-21 April 2017, Melbourne, VIC, Australia.

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Abstract

Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for melanoma diagnosis. There have many attempts to segment skin lesions in a semi- or fully-automated manner. Existing methods, however, have problems with over- or under-segmentation and do not perform well with challenging skin lesions such as when a lesion is partially connected to the background or when image contrast is low. To overcome these limitations, we propose a new semi-automated skin lesion segmentation method that incorporates fully convolutional networks (FCNs) with multi-scale integration. We leverage the use of FCNs to derive high-level semantic information with simple user interaction e.g., a single click to accurately segment skin lesions of various complexity. Our experiments with 379 skin lesion images show that our proposed method achieves better segmentation results when compared to the state-of-the-art skin lesion segmentation methods for challenging skin lesions.

Item ID:	72038
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-5090-1172-8
Copyright Information:	© 2017 IEEE
Date Deposited:	12 May 2022 02:36
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 > 220408 Information systems @ 100%
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