Linked Life Data

20800995

Source:http://linkedlifedata.com/resource/pubmed/id/20800995

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2011-2-15
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1879-0771
pubmed:author
pubmed:copyrightInfo
Copyright © 2010 Elsevier Ltd. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
35
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
128-36
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Fast density-based lesion detection in dermoscopy images.
pubmed:affiliation
Department of Computer Science, Texas A&M University-Commerce, USA. mutlu mete@tamu-commerce.edu
pubmed:publicationType
Journal Article