Segmentation of Skin Lesions From Digital Images Using Joint Statistical Texture Distinctiveness
- 2 January 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Biomedical Engineering
- Vol. 61 (4), 1220-1230
- https://doi.org/10.1109/tbme.2013.2297622
Abstract
Melanoma is the deadliest form of skin cancer. Incidence rates of melanoma have been increasing, especially among non-Hispanic white males and females, but survival rates are high if detected early. Due to the costs for dermatologists to screen every patient, there is a need for an automated system to assess a patient's risk of melanoma using images of their skin lesions captured using a standard digital camera. One challenge in implementing such a system is locating the skin lesion in the digital image. A novel texture-based skin lesion segmentation algorithm is proposed. A set of representative texture distributions are learned from an illumination-corrected photograph and a texture distinctiveness metric is calculated for each distribution. Next, regions in the image are classified as normal skin or lesion based on the occurrence of representative texture distributions. The proposed segmentation framework is tested by comparing lesion segmentation results and melanoma classification results to results using other state-of-art algorithms. The proposed framework has higher segmentation accuracy compared to all other tested algorithms.Keywords
Funding Information
- Agfa Healthcare Inc.
- Ontario Ministry of Research and Innovation
- Ontario Centres of Excellence
- the Natural Sciences and Engineering Research Council of Canada
- Canada Research Chairs Program
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