? Although commercial computer-aided detection (CAD) products have been used in a large number of medical institutions to assist radiologists in screening mammography, radiologists often have limited confidence in CAD cueing results for masses due to the relatively low sensitivity and reproducibility, as well as the higher false-positive rate, than that compared with the performance for microcalcification detection and characterization. There is no general agreement on whether and how a CAD system can best help improve radiologists' diagnostic performance. To address these issues, we propose to develop and evaluate a unique Interactive Computer-Aided Detection (ICAD) system for mammography. In addition to providing initial cues on the processed images as would current systems, radiologists can interact with the ICAD system in several ways. Observers can select any regions (cued or not cued) on the mammogram and query ICAD. Upon receiving a request from the observer, ICAD will extract the region, compute a feature vector, compare the vector with a large number of regions in a reference library, and generate a classification score using a weighted k-nearest neighbor algorithm. In addition, upon request, the system will provide the results of a scheme specifically optimized on """"""""the latest prior images"""""""" to enable observers to assess """"""""early signs"""""""" for abnormalities that may develop and later (on subsequent examinations) prove to be malignant. To test such a hypothesis, an observer performance study will also be carried out in this project to compare the performance difference when six radiologists use a CAD system operating at the performance level of leading commercial products and our newly proposed ICAD system. ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA101733-02
Application #
6929774
Study Section
Diagnostic Imaging Study Section (DMG)
Program Officer
Croft, Barbara
Project Start
2004-09-01
Project End
2008-06-30
Budget Start
2005-09-01
Budget End
2006-06-30
Support Year
2
Fiscal Year
2005
Total Cost
$168,444
Indirect Cost
Name
University of Pittsburgh
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
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Wang, Xiao Hui; Park, Sang Cheol; Zheng, Bin (2011) Assessment of performance and reliability of computer-aided detection scheme using content-based image retrieval approach and limited reference database. J Digit Imaging 24:352-9
Zheng, Bin; Wang, Xingwei; Lederman, Dror et al. (2010) Computer-aided detection; the effect of training databases on detection of subtle breast masses. Acad Radiol 17:1401-8
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Park, Sang Cheol; Wang, Xiao-Hui; Zheng, Bin (2009) Assessment of performance improvement in content-based medical image retrieval schemes using fractal dimension. Acad Radiol 16:1171-8
Park, Sang Cheol; Pu, Jiantao; Zheng, Bin (2009) Improving performance of computer-aided detection scheme by combining results from two machine learning classifiers. Acad Radiol 16:266-74
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Zheng, Bin (2009) Computer-Aided Diagnosis in Mammography Using Content-based Image Retrieval Approaches: Current Status and Future Perspectives. Algorithms 2:828-849
Wang, Xiao-Hui; Park, Sang Cheol; Zheng, Bin (2009) Improving performance of content-based image retrieval schemes in searching for similar breast mass regions: an assessment. Phys Med Biol 54:949-61
Jiang, Luan; Song, Enmin; Xu, Xiangyang et al. (2008) Automated detection of breast mass spiculation levels and evaluation of scheme performance. Acad Radiol 15:1534-44

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