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      Automated saliency-based lesion segmentation in dermoscopic images.

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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.

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          Author and article information

          Journal
          Annu Int Conf IEEE Eng Med Biol Soc
          Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
          Institute of Electrical and Electronics Engineers (IEEE)
          2694-0604
          2375-7477
          Aug 2015
          : 2015
          Article
          10.1109/EMBC.2015.7319025
          26736925
          b2856552-220d-43df-92cc-93ac502feb38
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