Opacity detection and characterization in mammograms using bilateral comparison and local characteristics

Jean-Marc Dinten; Guillaume Montemont; Michel Darboux

doi:10.1117/12.274168

25 April 1997 Opacity detection and characterization in mammograms using bilateral comparison and local characteristics

Jean-Marc Dinten, Guillaume Montemont, Michel Darboux

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274168
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

Detection of opacities in mammograms, and especially of spicularities, is an important point for an early detection of breast cancer. Because of the superimposition of complex structures in a mammogram, it is a very tricky task. In this paper we propose a detection scheme combining, on one hand, information provided by an analysis of each single mammogram, and on the other hand, information provided by a comparison between the right and left mammograms. At first the two mammograms are filtered and registered, the potential pathological sites are obtained on the basis of a distance criterion adapted to opacities detection between the two mammograms. Then a robust segmentation method delimits a region of interest (ROI) surrounding each potential pathological site. To limit the number of false positives and to provide the physicians with quantitative parameters, each detected region is characterized by a set of four parameters. This global approach has been evaluated on mammograms of the MIAS database, representative of different opacities shapes and different backgrounds. The results have shown that all sites identified as malignant have been detected with a low rate of false detections.

Citation Download Citation

Jean-Marc Dinten, Guillaume Montemont, and Michel Darboux "Opacity detection and characterization in mammograms using bilateral comparison and local characteristics", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274168

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