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Proceedings Paper

Improved method for detection of microcalcification clusters in digital mammograms
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Paper Abstract

In this study it is shown that the performance of a statistical method for detection of microcalcification clusters in digital mammograms, can be improved substantially by using a second step of classification. During this second step, detected clusters are automatically classified into true positive and false positive detected clusters. For classification the k-nearest neighbor method was used in a leave-one-patient-out procedure. The sensitivity level of the method was adjusted both in the first detection step as in the second classification step. The Mahalanobis distance was used as criterion in the sequential forward selection procedure for selection of features. This primary feature selection method was combined with a classification performance criterion for the final feature selection. By applying the initial detection at various levels of sensitivity, various sets of false and true positive detected clusters were created. At each of these sets the classification ca be performed. Results show that the overall best FROC performance after secondary classification is obtained by varying sensitivity levels in both the first and second step. Furthermore, it was shown that performing a new feature selection for each different set of false and true positives is essential. A large database of 245 digitized mammograms with 341 clusters was used for evaluation of the method.

Paper Details

Date Published: 21 May 1999
PDF: 11 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348607
Show Author Affiliations
Wouter J. H. Veldkamp, Radboud Univ. Hospital (Netherlands)
Nico Karssemeijer, Radboud Univ. Hospital (Netherlands)


Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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