Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis
dc.contributor.author | Petrosian, A. | en_US |
dc.contributor.author | Chan, Heang-Ping | en_US |
dc.contributor.author | Helvie, Mark A. | en_US |
dc.contributor.author | Goodsitt, Mitchell M. | en_US |
dc.contributor.author | Adler, Dorit D. | en_US |
dc.date.accessioned | 2006-12-19T19:02:39Z | |
dc.date.available | 2006-12-19T19:02:39Z | |
dc.date.issued | 1994-12-01 | en_US |
dc.identifier.citation | Petrosian, 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.issn | 0031-9155 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/48958 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=15551553&dopt=citation | en_US |
dc.description.abstract | Computer-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.extent | 3118 bytes | |
dc.format.extent | 772062 bytes | |
dc.format.mimetype | text/plain | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.publisher | IOP Publishing Ltd | en_US |
dc.title | Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Physics | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationother | Dept. of Radiol., Michigan Univ., Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationother | Dept. of Radiol., Michigan Univ., Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationother | Dept. of Radiol., Michigan Univ., Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationother | Dept. of Radiol., Michigan Univ., Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationother | Dept. of Radiol., Michigan Univ., Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.identifier.pmid | 15551553 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/48958/2/pb941210.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1088/0031-9155/39/12/010 | en_US |
dc.identifier.source | Physics in Medicine and Biology. | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
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.