Linked Life Data

15890256

Source:http://linkedlifedata.com/resource/pubmed/id/15890256

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2005-5-13
pubmed:abstractText
The sensitivity and specificity of melanoma diagnosis can be improved by adding the lesion depth and structure information obtained from the multi-spectral, trans-illumination images to the surface characteristic information obtained from the epi-illumination images. Wavelet transform based bi-modal channel energy features obtained from the images are used in the analysis. Methods using both crisp and fuzzy membership based partitioning of the feature space are evaluated. For this purpose, the ADWAT classification method that uses crisp partitioning is extended to handle multi-spectral image data. Also, multi-dimensional fuzzy membership functions with Gaussian and Bell profiles are proposed for classification. Results show that the fuzzy membership functions with Bell profile are more effective than the extended ADWAT method in discriminating melanoma from dysplastic nevus.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0895-6111
pubmed:author
pubmed:issnType
Print
pubmed:volume
29
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
287-96
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Multi-spectral image analysis and classification of melanoma using fuzzy membership based partitions.
pubmed:affiliation
Department of Electrical and Computer Engineering, New Jersey Institute of Technology, 323 Martin Luther King Blvd., University Heights, Newark, NJ 07102, USA.
pubmed:publicationType
Journal Article