The detection of micro-calcifications in mammographic images using high dimensional features

This paper examines techniques for the efficient use of high dimensional feature sets in the detection of micro-calcifications in mammograms. The paper focuses on techniques for dimensionality reduction and discriminant analysis. The paper examines the use of principal components and Fisher's l...

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Bibliographic Details
Published inProceedings of IEEE Symposium on Computer-Based Medical Systems (CBMS) pp. 139 - 145
Main Authors Solka, J.L., Poston, W.L., Priebe, C.E., Rogers, G.W., Lorey, R.A., Marchette, D.J., Woods, K., Bowyer, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE Comput. Soc. Press 1994
Subjects
Background noise
Breast cancer
Diagnostic radiography
Feature extraction
Fluctuations
Image segmentation
Military computing
Pixel
Statistics
Weapons
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Summary:This paper examines techniques for the efficient use of high dimensional feature sets in the detection of micro-calcifications in mammograms. The paper focuses on techniques for dimensionality reduction and discriminant analysis. The paper examines the use of principal components and Fisher's linear discriminant for dimensionality reduction along with parametric and nonparametric statistical techniques for discriminant analysis.< >
ISBN:9780818662560
0818662565
DOI:10.1109/CBMS.1994.316001