Paper
29 July 1993 Classification of ductal carcinoma in-situ by image analysis of calcifications from mammograms
Jon Parker, David R. Dance, David H. Davies, L. J. Yeoman, M. J. Michell, S. Humphreys
Author Affiliations +
Proceedings Volume 1905, Biomedical Image Processing and Biomedical Visualization; (1993) https://doi.org/10.1117/12.148695
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States
Abstract
Image analysis methods have been developed to characterize calcifications associated with Ductal Carcinoma in-Situ (DCIS), and to differentiate between those having comedo or non- comedo histology. Cases were selected from the U.K. breast screening program, and in each case the histology and a magnified mammographic view were obtained. The films were digitized at 25 micron sampling size and 8 bit grey level resolution. Calcifications were manually segmented from the normal breast background, and a radiologist, experienced in breast screening, checked the labelling of a calcifications. An algorithm was developed to classify firstly the individual objects within a film, and secondly the film itself. The algorithm automatically selected the combination of features giving the least estimated Bayes error for a set of object-oriented features evaluated for each calcification. The k-nearest neighbors statistical approach was then used to classify individual objects giving a ratio of comedo to non-comedo objects for a set of training films. Films were classified by applying a threshold to this ratio. In the classification of typical comedo from typical non-comedo the success rate of the algorithm was 100% for a training set of 4 cases and test set of 16 cases.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jon Parker, David R. Dance, David H. Davies, L. J. Yeoman, M. J. Michell, and S. Humphreys "Classification of ductal carcinoma in-situ by image analysis of calcifications from mammograms", Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); https://doi.org/10.1117/12.148695
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Cited by 3 scholarly publications.
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KEYWORDS
Breast

Mammography

Image analysis

Algorithm development

Error analysis

Image classification

Image segmentation

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