Automated classification of parenchymal patterns in mammograms

Abstract

A method for automated determination of parenchymal patterns in mammograms has been developed that is insensitive to changes in the mammographic imaging technique. The method was designed to study the relation between breast cancer risk and changes of mammographic density. It includes a new method for automatic segmentation of the pectoral muscle in oblique mammograms, based on application of the Hough transform. The technique developed for classification of parenchymal patterns is based on a distance transform that subdivides the breast tissue area into regions in which distance to the skin line is approximately equal. Features are calculated from grey level histograms computed in these regions. In this way, dependency on varying tissue thickness in the peripheral zone of the breast is minimized. Additional features represent differences between tissue projected in pectoral and breast area. Robustness and classification performance were studied on a test set of 615 digitized mammograms, applying a kNN classifier and leave-one-out for training. Using four density categories in 67% of the cases an exact agreement was obtained with a subjective classification made by a radiologist. The number of cases for which classifications of the radiologist and the program differed by more that one category was only 2%. For more recent mammograms, recorded after 1991, an exact agreement of 80% was obtained.

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