Detection of Cancerous Masses in Mammograms by Template Matching: Optimization of Template Brightness Distribution by Means of Evolutionary Algorithm.
Optimization of brightness distribution in the template used for detection of cancerous masses in mammograms by means of correlation coefficient is presented. This optimization is performed by the evolutionary algorithm using an auxiliary mass classifier. Brightness along the radius of the circularl...
| Publicado en: | Journal of Digital Imaging Vol. 25; no. 1; pp. 162 - 173 |
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| Autores principales: | , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104634137&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104634137 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2012 vid: 25 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104634137 70531115 10.1007/s10278-011-9402-1 NLM21748410 PMC3264718 104634137 ppf: 162 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Detection of Cancerous Masses in Mammograms by Template Matching: Optimization of Template Brightness Distribution by Means of Evolutionary Algorithm. aug: au: Bator, Marcin Nieniewski, Mariusz affil: Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, ul. Nowoursynowska 159 02776 Warsaw Poland sug: subj: Mammography Breast Neoplasms Diagnosis Diagnosis, Computer Assisted Human ROC Curve Algorithms False Positive Results Confidence Intervals Evaluation Research ab: Optimization of brightness distribution in the template used for detection of cancerous masses in mammograms by means of correlation coefficient is presented. This optimization is performed by the evolutionary algorithm using an auxiliary mass classifier. Brightness along the radius of the circularly symmetric template is coded indirectly by its second derivative. The fitness function is defined as the area under curve (AUC) of the receiver operating characteristic (ROC) for the mass classifier. The ROC and AUC are obtained for a teaching set of regions of interest (ROIs), for which it is known whether a ROI is true-positive (TP) or false-positive (F). The teaching set is obtained by running the mass detector using a template with a predetermined brightness. Subsequently, the evolutionary algorithm optimizes the template by classifying masses in the teaching set. The optimal template (OT) can be used for detection of masses in mammograms with unknown ROIs. The approach was tested on the training and testing sets of the Digital Database for Screening Mammography (DDSM). The free-response receiver operating characteristic (FROC) obtained with the new mass detector seems superior to the FROC for the hemispherical template (HT). Exemplary results are the following: in the case of the training set in the DDSM, the true-positive fraction (TPF) = 0.82 for the OT and 0.79 for the HT; in the case of the testing set, TPF = 0.79 for the OT and 0.72 for the HT. These values were obtained for disease cases, and the false-positive per image (FPI) = 2. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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