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Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images | IEEE Journals & Magazine | IEEE Xplore

Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images


Abstract:

The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis of melanoma. Conventional segmentation methods, however...Show More

Abstract:

The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in underand oversegmentation of the skin lesion. We suggest that saliency detection using the reconstruction errors derived from a sparse representation model coupled with a novel background detection can more accurately discriminate the lesion from surrounding regions. We further propose a Bayesian framework that better delineates the shape and boundaries of the lesion. We also evaluated our approach on two public datasets comprising 1100 dermoscopic images and compared it to other conventional and state-of-the-art unsupervised (i.e., no training required) lesion segmentation methods, as well as the state-of-the-art unsupervised saliency detection methods. Our results show that our approach is more accurate and robust in segmenting lesions compared to other methods. We also discuss the general extension of our framework as a saliency optimization algorithm for lesion segmentation.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 21, Issue: 6, November 2017)
Page(s): 1685 - 1693
Date of Publication: 16 January 2017

ISSN Information:

PubMed ID: 28092585

Funding Agency:


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