Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRI
An application of an unsupervised neural network-based computer-aided diagnosis (CAD) system is reported for the detection and characterization of small indeterminate breast lesions, average size 1.1 mm, in dynamic contrast-enhanced MRI. This system enables the extraction of spatial and temporal fea...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2009-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2009/326924 |
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| author | A. Meyer-Baese T. Schlossbauer O. Lange A. Wismueller |
| author_facet | A. Meyer-Baese T. Schlossbauer O. Lange A. Wismueller |
| author_sort | A. Meyer-Baese |
| collection | DOAJ |
| description | An application of an unsupervised neural network-based computer-aided diagnosis (CAD) system is reported for the detection
and characterization of small indeterminate breast lesions, average size 1.1 mm, in dynamic contrast-enhanced MRI. This system
enables the extraction of spatial and temporal features of dynamic MRI data and additionally provides a segmentation with regard
to identification and regional subclassification of pathological breast tissue lesions. Lesions with an initial contrast enhancement
≥50% were selected with semiautomatic segmentation. This conventional segmentation analysis is based on the mean initial signal
increase and postinitial course of all voxels included in the lesion. In this paper, we compare the conventional segmentation analysis
with unsupervised classification for the evaluation of signal intensity time courses for the differential diagnosis of enhancing
lesions in breast MRI. The results suggest that the computerized analysis system based on unsupervised clustering has the potential to
increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis
of breast cancer with MR mammography. |
| format | Article |
| id | doaj-art-0a02813dcf014eb19e37be972a2d7db8 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2009-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-0a02813dcf014eb19e37be972a2d7db82025-08-20T03:55:27ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962009-01-01200910.1155/2009/326924326924Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRIA. Meyer-Baese0T. Schlossbauer1O. Lange2A. Wismueller3Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32310, USAInstitute for Clinical Radiology, University of Munich, 81377 Munich, GermanyDepartment of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32310, USADepartment of Biomedical Engineering, University of Rochester, Rochester, NY 14642, USAAn application of an unsupervised neural network-based computer-aided diagnosis (CAD) system is reported for the detection and characterization of small indeterminate breast lesions, average size 1.1 mm, in dynamic contrast-enhanced MRI. This system enables the extraction of spatial and temporal features of dynamic MRI data and additionally provides a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions. Lesions with an initial contrast enhancement ≥50% were selected with semiautomatic segmentation. This conventional segmentation analysis is based on the mean initial signal increase and postinitial course of all voxels included in the lesion. In this paper, we compare the conventional segmentation analysis with unsupervised classification for the evaluation of signal intensity time courses for the differential diagnosis of enhancing lesions in breast MRI. The results suggest that the computerized analysis system based on unsupervised clustering has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography.http://dx.doi.org/10.1155/2009/326924 |
| spellingShingle | A. Meyer-Baese T. Schlossbauer O. Lange A. Wismueller Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRI International Journal of Biomedical Imaging |
| title | Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRI |
| title_full | Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRI |
| title_fullStr | Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRI |
| title_full_unstemmed | Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRI |
| title_short | Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRI |
| title_sort | small lesions evaluation based on unsupervised cluster analysis of signal intensity time courses in dynamic breast mri |
| url | http://dx.doi.org/10.1155/2009/326924 |
| work_keys_str_mv | AT ameyerbaese smalllesionsevaluationbasedonunsupervisedclusteranalysisofsignalintensitytimecoursesindynamicbreastmri AT tschlossbauer smalllesionsevaluationbasedonunsupervisedclusteranalysisofsignalintensitytimecoursesindynamicbreastmri AT olange smalllesionsevaluationbasedonunsupervisedclusteranalysisofsignalintensitytimecoursesindynamicbreastmri AT awismueller smalllesionsevaluationbasedonunsupervisedclusteranalysisofsignalintensitytimecoursesindynamicbreastmri |