Comparison of feature extraction and selection methods in mammogram recognition.
This paper presents a comparison of feature extraction and selection methods in the design of mammogram recognition systems. Mammographic images were classified into two categories, normal and cancerous. The following methods of feature extraction were investigated: two-dimensional Haar wavelets, histograms, and singular value decomposition. The feature patterns were reduced and selected using principal component analysis (PCA) and rough sets. The rough sets methods were applied to the final selection of the pattern features. Classification of mammograms was realized using an error backpropagation neural network.
Swiniarski
R
R
Department of Computer Science, San Diego State University, California 92182, USA. rswiniar@sciences.sdsu.edu
Swiniarska
A
A
D001940:Breast / Q000166:cytology; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated; D012016:Reference Values