A method is presented for estimation of dense breast tissue volume from mammograms obtained with full-field digital mammography (FFDM). The thickness of dense tissue mapping to a pixel is determined by using a physical model of image acquisition. This model is based on the assumption that the breast is composed of two types of tissue, fat and parenchyma. Effective linear attenuation coefficients of these tissues are derived from empirical data as a function of tube voltage (kVp), anode material, filtration, and compressed breast thickness. By employing these, tissue composition at a given pixel is computed after performing breast thickness compensation, using a reference value for fatty tissue determined by the maximum pixel value in the breast tissue projection. Validation has been performed using 22 FFDM cases acquired with a GE Senographe 2000D by comparing the volume estimates with volumes obtained by semi-automatic segmentation of breast magnetic resonance imaging (MRI) data. The correlation between MRI and mammography volumes was 0.94 on a per image basis and 0.97 on a per patient basis. Using the dense tissue volumes from MRI data as the gold standard, the average relative error of the volume estimates was 13.6%.
Mammogram registration is an important technique to optimize the display of cases on a digital viewing station, and to find corresponding regions in temporal pairs of mammograms for computer-aided diagnosis algorithms. Four methods for mammogram registration were tested and results were compared. The performance of all registration methods was measured by comparing the distance between annotations of abnormalities in the previous and current view before and after registration. Registration by mutual information outperformed alignment based on nipple location, alignment based on center of mass of breast tissue, and warping.
Comparison with prior mammograms significantly improves overall performance and can reduce referrals due to nonlesion locations. Limiting the availability of prior mammograms to cases selected by the reader reduces the beneficial effect of prior mammograms.
Peripheral enhancement and tilt correction of unprocessed digital mammograms was achieved with a new reversible algorithm. This method has two major advantages for image visualization. First, the display dynamic range can be relatively small, and second, adjustment of the overall luminance to inspect details is not required in most cases. The correction is useful for preprocessing in computer-aided detection/diagnosis algorithms. The method is based on knowledge of the three-dimensional compressed breast shape to equalize thickness by adding virtual tissue, which results in intensity equalization for the mammographic image. Previously described methods implicitly estimate the contribution of thickness variations to image intensity, usually by nonparametric methods. The proposed method employs a global parametic breast shape model, which is advantageous for visualization and CAD.
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