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Identifying High Order Brain Connectome Biomarkers via Learning on Hypergraph Read chapter Read first chapter

2016 | OriginalPaper | Chapter

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

Authors : Behzad Bozorgtabar, Mani Abedini, Rahil Garnavi

Published in: Machine Learning in Medical Imaging

Publisher: 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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Footnotes
1
m is the feature dimension.
 
2
\(w_{ij}=exp\left( -\frac{\left\| y_{i},y_{j} \right\| }{\sigma ^{2}} \right) \quad s.t. \quad j\in NB\left( i \right) \).
 
3
The parameter \(\beta \) is set to 0.2.
 
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Metadata
Title
Sparse Coding Based Skin Lesion Segmentation Using Dynamic Rule-Based Refinement
Authors
Behzad Bozorgtabar
Mani Abedini
Rahil Garnavi
Copyright Year
2016
Book
Machine Learning in Medical Imaging

Print ISBN: 978-3-319-47156-3

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

Copyright Year: 2016

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

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