The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Automated classification of parenchymal patterns in mammograms
Abstract
A method for automated determination of parenchymal patterns in mammograms has been developed that is insensitive to changes in the mammographic imaging technique. The method was designed to study the relation between breast cancer risk and changes of mammographic density. It includes a new method for automatic segmentation of the pectoral muscle in oblique mammograms, based on application of the Hough transform. The technique developed for classification of parenchymal patterns is based on a distance transform that subdivides the breast tissue area into regions in which distance to the skin line is approximately equal. Features are calculated from grey level histograms computed in these regions. In this way, dependency on varying tissue thickness in the peripheral zone of the breast is minimized. Additional features represent differences between tissue projected in pectoral and breast area. Robustness and classification performance were studied on a test set of 615 digitized mammograms, applying a kNN classifier and leave-one-out for training. Using four density categories in 67% of the cases an exact agreement was obtained with a subjective classification made by a radiologist. The number of cases for which classifications of the radiologist and the program differed by more that one category was only 2%. For more recent mammograms, recorded after 1991, an exact agreement of 80% was obtained.
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Cites
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Boyd, N. F., Byng, J. W., Jong, R. A., Fishell, E. K., Little, L. E., Miller, A. B., Lockwood, G. A., Tritchler, D. L., & Yaffe, M. J. (1995) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/87.9.670 · Cited by 1,105
Risk for breast cancer development determined by mammographic parenchymal pattern
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The quantitative analysis of mammographic densities
Byng, J. W., Boyd, N. F., Fishell, E., Jong, R. A., & Yaffe, M. J. (1994) · Physics in Medicine and Biology · doi:10.1088/0031-9155/39/10/008 · Cited by 607
Automated analysis of mammographic densities
Byng, J. W., Boyd, N. F., Fishell, E., Jong, R. A., & Yaffe, M. J. (1996) · Physics in Medicine and Biology · doi:10.1088/0031-9155/41/5/007 · Cited by 208
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Cited by
Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T. H., Chevalier, M., Tan, T., Mertelmeier, T., et al. (2019) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/djy222 · Cited by 692
Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System
Rodríguez-Ruiz, A., Krupinski, E., Mordang, J.-J., Schilling, K., Heywang-Köbrunner, S. H., Sechopoulos, I., & Mann, R. M. (2019) · Radiology · doi:10.1148/radiol.2018181371 · Cited by 602
A review of automatic mass detection and segmentation in mammographic images
Oliver, A., Freixenet, J., Martí, J., Pérez, E., Pont, J., Denton, E. R. E., & Zwiggelaar, R. (2010) · Medical Image Analysis · doi:10.1016/j.media.2009.12.005 · Cited by 398
A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs
Rangayyan, R. M., Ayres, F. J., & Leo Desautels, J. E. (2007) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2006.09.003 · Cited by 337
Metadata
- Authors
- Karssemeijer, N.
- Venue
- Physics in Medicine and Biology
- Publisher
- IOP Publishing
- Published
- 1998-02-01
- Volume
- 43
- Issue
- 2
- Pages
- 365-378
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- OpenAlex
- W2040434704
- Cited by
- 277
- References
- 8
- FWCI
- 4.63
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
