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

CITATIONS