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2016 | OriginalPaper | Buchkapitel

Sparse Coding Based Skin Lesion Segmentation Using Dynamic Rule-Based Refinement

verfasst von : Behzad Bozorgtabar, Mani Abedini, Rahil Garnavi

Erschienen in: Machine Learning in Medical Imaging

Verlag: Springer International Publishing

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Abstract

This paper proposes an unsupervised skin lesion segmentation method for dermoscopy images by exploiting the contextual information of skin image at the superpixel level. In particular, a Laplacian sparse coding is presented to evaluate the probabilities of the skin image pixels to delineate lesion border. Moreover, a new rule-based smoothing strategy is proposed as the lesion segmentation refinement procedure. Finally, a multi-scale superpixel segmentation of the skin image is provided to handle size variation of the lesion in order to improve the accuracy of the detected border. Experiments conducted on two datasets show the superiority of our proposed method over several state-of-the-art skin segmentation methods.

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Nächstes Kapitel Structure Fusion for Automatic Segmentation of Left Atrial Aneurysm Based on Deep Residual Networks

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Titel: Sparse Coding Based Skin Lesion Segmentation Using Dynamic Rule-Based Refinement
verfasst von: Behzad Bozorgtabar
Mani Abedini
Rahil Garnavi
Verlag: Springer International Publishing
Buch: Machine Learning in Medical Imaging
Print ISBN: 978-3-319-47156-3

Electronic ISBN: 978-3-319-47157-0

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-47157-0_31

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