Feature selection using general regression neural networks for the automatic detection of clustered microcalcifications
Songyang Yu, Ling Guan · 1999
General regression neural networks (GRNNs) are proposed for selecting the most discriminating features for the automatic detection of clustered microcalcifications in digital mammograms. Previously, We have designed an image processing system for detecting clustered microcalcifications. The system uses wavelet coefficients and feed forward neural networks to identify possible microcalcification pixels and a set of structure features to locate individual microcalcifications. In this work, more features are extracted, and the most discriminating features are selected through the analysis of the GRNNs. The selected features are incorporated into our image processing system and applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications. Free response operating characteristics (FROG) curves are used to evaluate the performance. Results show that, by incorporating the proposed feature selection scheme, the performance of our system is improved significantly.