Show simple item record

Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network

dc.contributor.authorChan, Heang-Pingen_US
dc.contributor.authorSahiner, Berkmanen_US
dc.contributor.authorPetrick, Nicholas A.en_US
dc.contributor.authorHelvie, Mark A.en_US
dc.contributor.authorLam, Kwok Leungen_US
dc.contributor.authorAdler, Dorit D.en_US
dc.contributor.authorGoodsitt, Mitchell M.en_US
dc.date.accessioned2006-12-19T19:02:54Z
dc.date.available2006-12-19T19:02:54Z
dc.date.issued1997-03-01en_US
dc.identifier.citationChan, Heang-Ping; Sahiner, Berkman; Petrick, Nicholas; Helvie, Mark A; Lam, Kwok Leung; Adler, Dorit D; Goodsitt, Mitchell M (1997). "Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network ." Physics in Medicine and Biology. 42(3): 549-567. <http://hdl.handle.net/2027.42/48961>en_US
dc.identifier.issn0031-9155en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/48961
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=9080535&dopt=citationen_US
dc.description.abstractWe investigated the feasibility of using texture features extracted from mammograms to predict whether the presence of microcalcifications is associated with malignant or benign pathology. Eighty-six mammograms from 54 cases (26 benign and 28 malignant) were used as case samples. All lesions had been recommended for surgical biopsy by specialists in breast imaging. A region of interest (ROI) containing the microcalcifications was first corrected for the low-frequency background density variation. Spatial grey level dependence (SGLD) matrices at ten different pixel distances in both the axial and diagonal directions were constructed from the background-corrected ROI. Thirteen texture measures were extracted from each SGLD matrix. Using a stepwise feature selection technique, which maximized the separation of the two class distributions, subsets of texture features were selected from the multi-dimensional feature space. A backpropagation artificial neural network (ANN) classifier was trained and tested with a leave-one-case-out method to recognize the malignant or benign microcalcification clusters. The performance of the ANN was analysed with receiver operating characteristic (ROC) methodology. It was found that a subset of six texture features provided the highest classification accuracy among the feature sets studied. The ANN classifier achieved an area under the ROC curve of 0.88. By setting an appropriate decision threshold, 11 of the 28 benign cases were correctly identified (39% specificity) without missing any malignant cases (100% sensitivity) for patients who had undergone biopsy. This preliminary result indicates that computerized texture analysis can extract mammographic information that is not apparent by visual inspection. The computer-extracted texture information may be used to assist in mammographic interpretation, with the potential to reduce biopsies of benign cases and improve the positive predictive value of mammography.en_US
dc.format.extent3118 bytes
dc.format.extent299012 bytes
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherIOP Publishing Ltden_US
dc.titleComputerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural networken_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelPhysicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, MI, USAen_US
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, MI, USAen_US
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, MI, USAen_US
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, MI, USAen_US
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, MI, USAen_US
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, MI, USAen_US
dc.contributor.affiliationumDepartment of Radiology, University of Michigan, Ann Arbor, MI, USAen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.identifier.pmid9080535en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/48961/2/m70308.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1088/0031-9155/42/3/008en_US
dc.identifier.sourcePhysics in Medicine and Biology.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Accessibility: If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.