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Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers (1994)
Pacific Grove, CA, USA
Oct. 31, 1994 to Nov. 2, 1994
ISSN: 1058-6393
ISBN: 0-8186-6405-3
pp: 716-720
C.L. Nash , Inf. Syst. Lab., Stanford Univ., CA, USA
K.O. Perlmutter , Inf. Syst. Lab., Stanford Univ., CA, USA
R.M. Gray , Inf. Syst. Lab., Stanford Univ., CA, USA
ABSTRACT
The automated detection of suspicious tissue in digital mammograms can provide a useful aid to diagnosis by permitting a radiologist to see all regions deemed suspicious by the computer. The authors apply to digital mammography a method that combines aspects of data compression techniques based on clustering and decision trees together with algorithms for classification and regression. The idea is to use a distortion measure in a clustering algorithm that includes both squared error for general appearance and average Bayes risk for classification accuracy. The algorithm structure is that of a vector quantization compression system that incorporates Bayes risk into the optimization algorithm.<>
INDEX TERMS
diagnostic radiography, biomedical imaging, vector quantisation, Bayes methods, trees (mathematics), image classification, medical image processing
CITATION

C. Nash, K. Perlmutter and R. Gray, "Evaluation of Bayes risk weighted vector quantization with posterior estimation in the detection of lesions in digitized mammograms," Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers(ACSSC), Pacific Grove, CA, USA, 1995, pp. 716-720.
doi:10.1109/ACSSC.1994.471545
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