An Interactive System for Computer-Aided Diagnosis of Breast Masses.
Although mammography is the only clinically accepted imaging modality for screening the general population to detect breast cancer, interpreting mammograms is difficult with lower sensitivity and specificity. To provide radiologists 'a visual aid' in interpreting mammograms, we developed and tested...
| Publicado en: | Journal of Digital Imaging Vol. 25; no. 5; pp. 570 - 580 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2012
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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=104416607&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104416607 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2012 vid: 25 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104416607 80125157 10.1007/s10278-012-9451-0 NLM22234836 104416607 ppf: 570 ppct: 10 formats: fmt: @attributes: type: P tig: atl: An Interactive System for Computer-Aided Diagnosis of Breast Masses. aug: au: Wang, Xingwei Li, Lihua Liu, Wei Xu, Weidong Lederman, Dror Zheng, Bin affil: Department of Radiology, University of Pittsburgh, 3362 Fifth Avenue Pittsburgh 15213 USA sug: subj: Breast Neoplasms Diagnosis Diagnosis, Computer Assisted Mammography Decision Support Techniques Systems Design Algorithms Radiographic Image Enhancement Methods Evaluation Research ROC Curve Confidence Intervals P-Value Human Funding Source ab: Although mammography is the only clinically accepted imaging modality for screening the general population to detect breast cancer, interpreting mammograms is difficult with lower sensitivity and specificity. To provide radiologists 'a visual aid' in interpreting mammograms, we developed and tested an interactive system for computer-aided detection and diagnosis (CAD) of mass-like cancers. Using this system, an observer can view CAD-cued mass regions depicted on one image and then query any suspicious regions (either cued or not cued by CAD). CAD scheme automatically segments the suspicious region or accepts manually defined region and computes a set of image features. Using content-based image retrieval (CBIR) algorithm, CAD searches for a set of reference images depicting 'abnormalities' similar to the queried region. Based on image retrieval results and a decision algorithm, a classification score is assigned to the queried region. In this study, a reference database with 1,800 malignant mass regions and 1,800 benign and CAD-generated false-positive regions was used. A modified CBIR algorithm with a new function of stretching the attributes in the multi-dimensional space and decision scheme was optimized using a genetic algorithm. Using a leave-one-out testing method to classify suspicious mass regions, we compared the classification performance using two CBIR algorithms with either equally weighted or optimally stretched attributes. Using the modified CBIR algorithm, the area under receiver operating characteristic curve was significantly increased from 0.865 ± 0.006 to 0.897 ± 0.005 ( p < 0.001). This study demonstrated the feasibility of developing an interactive CAD system with a large reference database and achieving improved performance. 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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