| Fast density-based lesion detection in dermoscopy images. | |
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MedLine Citation:
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PMID: 20800995 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is automated detection of lesion borders. In this study, we introduce a border-driven density-based framework to identify skin lesion(s) in dermoscopy images. Unlike the conventional density-based clustering algorithms, proposed algorithm expands regions only at borders of a cluster that in turn speeds up the process without losing precision or recall. In our method, border regions are represented with one or more simple polygons at any time. We tested our algorithm on a dataset of 100 dermoscopy cases with multiple physicians' drawn ground truth borders. The results show that border error and f-measure of assessment averages out at 6.9% and 0.86 respectively. |
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Authors:
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Mutlu Mete; Sinan Kockara; Kemal Aydin |
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Publication Detail:
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Type: Journal Article Date: 2010-09-17 |
Journal Detail:
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Title: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Volume: 35 ISSN: 1879-0771 ISO Abbreviation: Comput Med Imaging Graph Publication Date: 2011 Mar |
Date Detail:
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Created Date: 2011-02-15 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8806104 Medline TA: Comput Med Imaging Graph Country: United States |
Other Details:
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Languages: eng Pagination: 128-36 Citation Subset: IM |
Copyright Information:
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Copyright © 2010 Elsevier Ltd. All rights reserved. |
Affiliation:
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Department of Computer Science, Texas A&M University-Commerce, United States. |
Export Citation:
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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