DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, JK | ko |
dc.contributor.author | Park, HyunWook | ko |
dc.date.accessioned | 2009-06-30T01:22:00Z | - |
dc.date.available | 2009-06-30T01:22:00Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 1999-03 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON MEDICAL IMAGING, v.18, no.3, pp.231 - 238 | - |
dc.identifier.issn | 0278-0062 | - |
dc.identifier.uri | http://hdl.handle.net/10203/9879 | - |
dc.description.abstract | Clustered microcalcifications on X-ray mammograms are an important sign for early detection of breast cancer. Texture-analysis methods can be applied to detect clustered microcalcifications in digitized mammograms. In this paper, a comparative study of texture-analysis methods is performed for the surrounding region-dependence method, which has been proposed by the authors, and conventional texture-analysis methods, such as the spatial gray-level dependence method, the gray-level run-length method, and the gray-level difference method. Textural features extracted by these methods are exploited to classify regions of interest (ROI's) into positive ROI's containing clustered microcalcifications and negative ROI's containing normal tissues. A three-layer backpropagation neural network is used as a classifier. The results of the neural network for the texture-analysis methods are evaluated by using a receiver operating-characteristics (ROC) analysis. The surrounding region-dependence method is shown to be superior to the conventional texture-analysis methods with respect to classification accuracy and computational complexity. | - |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | COMPUTER-AIDED DIAGNOSIS | - |
dc.subject | CLUSTERED MICROCALCIFICATIONS | - |
dc.subject | DIGITAL MAMMOGRAMS | - |
dc.subject | AUTOMATED DETECTION | - |
dc.subject | ROC | - |
dc.title | Statistical textural features for detection of microcalcifications in digitized mammograms | - |
dc.type | Article | - |
dc.identifier.wosid | 000080491100005 | - |
dc.identifier.scopusid | 2-s2.0-0032587727 | - |
dc.type.rims | ART | - |
dc.citation.volume | 18 | - |
dc.citation.issue | 3 | - |
dc.citation.beginningpage | 231 | - |
dc.citation.endingpage | 238 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON MEDICAL IMAGING | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Park, HyunWook | - |
dc.contributor.nonIdAuthor | Kim, JK | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | breast cancer | - |
dc.subject.keywordAuthor | clustered microcalcifications | - |
dc.subject.keywordAuthor | neural network | - |
dc.subject.keywordAuthor | texture analysis | - |
dc.subject.keywordPlus | COMPUTER-AIDED DIAGNOSIS | - |
dc.subject.keywordPlus | CLUSTERED MICROCALCIFICATIONS | - |
dc.subject.keywordPlus | DIGITAL MAMMOGRAMS | - |
dc.subject.keywordPlus | AUTOMATED DETECTION | - |
dc.subject.keywordPlus | ROC | - |
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