In this paper, we investigate the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms. Our detection algorithm consists of the combination of two different methods. The first, based on difference-image techniques and gaussianity statistical tests, finds out the most obvious signals. The second, is able to discover more subtle microcalcifications by exploiting a multiresolution analysis by means of the wavelet transform. We can separately tune the two methods, so that each one of them is able to detect signals with similar features. By combining signals coming out from the two parts through a logical OR operation, we can discover microcalcifications with different characteristics. Our algorithm yields a sensitivity of 91.4% with 0.4 false positive cluster per image on the 40 images of the Nijmegen database.

Bazzani A., Bollini D., Brancaccio R., Campanini R., Lanconelli N., Romani D., et al. (2000). System for automatic detection of clustered microcalcifications in digital mammograms. INTERNATIONAL JOURNAL OF MODERN PHYSICS C, 11(5), 901-912 [10.1142/S0129183100000808].

System for automatic detection of clustered microcalcifications in digital mammograms

Bazzani A.;Bollini D.;Brancaccio R.;Lanconelli N.;Bevilacqua A.
2000

Abstract

In this paper, we investigate the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms. Our detection algorithm consists of the combination of two different methods. The first, based on difference-image techniques and gaussianity statistical tests, finds out the most obvious signals. The second, is able to discover more subtle microcalcifications by exploiting a multiresolution analysis by means of the wavelet transform. We can separately tune the two methods, so that each one of them is able to detect signals with similar features. By combining signals coming out from the two parts through a logical OR operation, we can discover microcalcifications with different characteristics. Our algorithm yields a sensitivity of 91.4% with 0.4 false positive cluster per image on the 40 images of the Nijmegen database.
2000
Bazzani A., Bollini D., Brancaccio R., Campanini R., Lanconelli N., Romani D., et al. (2000). System for automatic detection of clustered microcalcifications in digital mammograms. INTERNATIONAL JOURNAL OF MODERN PHYSICS C, 11(5), 901-912 [10.1142/S0129183100000808].
Bazzani A.; Bollini D.; Brancaccio R.; Campanini R.; Lanconelli N.; Romani D.; Bevilacqua A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/879813
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