Karssemeijer N
University Hospital Nijmegen, Department of Radiology, The Netherlands.
Phys Med Biol. 1998 Feb;43(2):365-78. doi: 10.1088/0031-9155/43/2/011.
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.
已开发出一种用于自动确定乳房X光片中实质模式的方法，该方法对乳房X光成像技术的变化不敏感。该方法旨在研究乳腺癌风险与乳房X光密度变化之间的关系。它包括一种基于霍夫变换应用的斜位乳房X光片中胸肌自动分割新方法。为实质模式分类开发的技术基于距离变换，该变换将乳房组织区域细分为与皮肤线距离大致相等的区域。特征是根据在这些区域计算的灰度直方图计算得出的。通过这种方式，将乳房外围区域中不同组织厚度的依赖性降至最低。其他特征表示胸肌区域和乳房区域中投射的组织之间的差异。在一个由615张数字化乳房X光片组成的测试集上进行了稳健性和分类性能研究，应用kNN分类器并采用留一法进行训练。在67%的病例中使用四种密度类别时，与放射科医生的主观分类获得了完全一致的结果。放射科医生和程序的分类相差超过一个类别的病例数仅为2%。对于1991年以后记录的更新的乳房X光片，获得了80%的完全一致结果。
