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dc.contributor.authorPetrosian, A.en_US
dc.contributor.authorChan, Heang-Pingen_US
dc.contributor.authorHelvie, Mark A.en_US
dc.contributor.authorGoodsitt, Mitchell M.en_US
dc.contributor.authorAdler, Dorit D.en_US
dc.date.accessioned2006-12-19T19:02:39Z
dc.date.available2006-12-19T19:02:39Z
dc.date.issued1994-12-01en_US
dc.identifier.citationPetrosian, A; Chan, Heang-Ping; Helvie, M A; Goodsitt, M M; Adler, D D (1994). "Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis." Physics in Medicine and Biology. 39(12): 2273-2288. <http://hdl.handle.net/2027.42/48958>en_US
dc.identifier.issn0031-9155en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/48958
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=15551553&dopt=citationen_US
dc.description.abstractComputer-aided diagnosis schemes are being developed to assist radiologists in mammographic interpretation. In this study, the authors investigated whether texture features could be used to distinguish between mass and non-mass regions in clinical mammograms. Forty-five regions of interest (ROIs) containing true masses with various degrees of visibility and 135 ROIs containing normal breast parenchyma were extracted manually from digitized mammograms as case samples. Spatial-grey-level-dependence (SGLD) matrices of each ROI were calculated and eight texture features were calculated from the SGLD matrices. The correlation and class-distance properties of extracted texture features were analysed. Selected texture features were input into a modified decision-tree classification scheme. The performance of the classifier was evaluated for different feature combinations and orders of features on the tree. A classification accuracy of about 89% sensitivity and 76% specificity was obtained for ordered features, sum average, correlation, and energy, during the training procedure. With a leave-one-out method, the test result was about 76% sensitivity and 64% specificity. The results of this preliminary study demonstrate the feasibility of using texture information for classification of mass and normal breast tissue, which will be likely to be useful for classifying true and false detections in computer-aided diagnosis programmes.en_US
dc.format.extent3118 bytes
dc.format.extent772062 bytes
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherIOP Publishing Ltden_US
dc.titleComputer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysisen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelPhysicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationotherDept. of Radiol., Michigan Univ., Ann Arbor, MI, USAen_US
dc.contributor.affiliationotherDept. of Radiol., Michigan Univ., Ann Arbor, MI, USAen_US
dc.contributor.affiliationotherDept. of Radiol., Michigan Univ., Ann Arbor, MI, USAen_US
dc.contributor.affiliationotherDept. of Radiol., Michigan Univ., Ann Arbor, MI, USAen_US
dc.contributor.affiliationotherDept. of Radiol., Michigan Univ., Ann Arbor, MI, USAen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.identifier.pmid15551553en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/48958/2/pb941210.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1088/0031-9155/39/12/010en_US
dc.identifier.sourcePhysics in Medicine and Biology.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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