Linked Life Data

19162891

Source:http://linkedlifedata.com/resource/pubmed/id/19162891

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2009-2-16
pubmed:abstractText
This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing capsule endoscopic images. Endoscopic images possess rich information expressed by texture. Texture information can be efficiently extracted from medium scales of the wavelet transform. The set of features proposed in this paper to code textural information is named color wavelet covariance (CWC). CWC coefficients are based on the covariances of second order textural measures, an optimum subset of them is proposed. Third and forth order moments are added to cope with distributions that tend to become non-Gaussian, especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data containing 6 full endoscopic exams and reached 95% specificity and 93% sensitivity.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
2008
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1242-5
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Classification of endoscopic capsule images by using color wavelet features, higher order statistics and radial basis functions.
pubmed:affiliation
University of Minho, Industrial Electronics Department, Campus de Azurém 4800-058 Gimarães, Portugal. clima@dei.uminho.pt
pubmed:publicationType
Journal Article