Automated saliency-based lesion segmentation in dermoscopic images
Conference Publication ResearchOnline@JCUAbstract
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.
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Publication Name
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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ISBN/ISSN
978-1-4244-9271-8
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Pages Count
4
Location
Milan, Italy
Publisher
Institute of Electrical and Electronics Engineers
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Publisher Location
Piscataway, NJ, USA
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DOI
10.1109/EMBC.2015.7319025