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Automated saliency-based lesion segmentation in dermoscopic images | IEEE Conference Publication | IEEE Xplore

Automated saliency-based lesion segmentation in dermoscopic images


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...Show More

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.
Date of Conference: 25-29 August 2015
Date Added to IEEE Xplore: 05 November 2015
ISBN Information:

ISSN Information:

PubMed ID: 26736925
Conference Location: Milan, Italy

References

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