Computer-aided detection of architectural distortion in prior mammograms of interval cancer.
hitectural distortion is an important sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. This paper presents methods for the detection of architectural distortion in mammograms of interval cancer cases taken prior to the detec...
| Published in: | Journal of Digital Imaging Vol. 23; no. 5; pp. 611 - 632 |
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| Main Authors: | , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2010
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105109492&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105109492 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2010 vid: 23 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105109492 54120716 10.1007/s10278-009-9257-x 105109492 ppf: 611 ppct: 21 formats: fmt: @attributes: type: P tig: atl: Computer-aided detection of architectural distortion in prior mammograms of interval cancer. aug: au: Rangayyan RM Banik S Desautels JEL affil: Department of Electrical and Computer Engineering, Schulich School of Engineering, Calgary Canada T2N 1N4; ranga@ucalgary.ca sug: subj: Mammography Breast Neoplasms Diagnosis Breast Anatomy and Histology Diagnosis, Computer Assisted Radiographic Image Interpretation, Computer-Assisted Early Detection of Cancer Time Factors Human Logistic Regression Retrospective Design Diagnostic Errors ROC Curve Sensitivity and Specificity Radiographic Image Enhancement False Negative Results Linear Regression Digitizers T-Tests P-Value Funding Source ab: hitectural distortion is an important sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. This paper presents methods for the detection of architectural distortion in mammograms of interval cancer cases taken prior to the detection of breast cancer using Gabor filters, phase portrait analysis, fractal analysis, and texture analysis. The methods were used to detect initial candidates for sites of architectural distortion in prior mammograms of interval cancer and also normal control cases. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval cancer cases, including 301 ROIs related to architectural distortion, and from 52 prior mammograms of 13 normal cases. For each ROI, the fractal dimension and Haralick's texture features were computed. Feature selection was performed separately using stepwise logistic regression and stepwise regression. The best results achieved, in terms of the area under the receiver operating characteristics curve, with the features selected by stepwise logistic regression are 0.76 with the Bayesian classifier, 0.73 with Fisher linear discriminant analysis, 0.77 with an artificial neural network based on radial basis functions, and 0.77 with a support vector machine. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.80 at 7.6 false positives per image. The methods have good potential in detecting architectural distortion in mammograms of interval cancer cases. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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