A method is proposed for generating synthetic mammograms based upon simulations of breast tissue and the mammographic imaging process. A computer breast model has been designed with a realistic distribution of large and medium scale tissue structures. Parameters controlling the size and placement of simulated structures (adipose compartments and ducts) provide a method for consistently modeling images of the same simulated breast with modified position or acquisition parameters. The mammographic imaging process is simulated using a compression model and a model of the x-ray image acquisition process. The compression model estimates breast deformation using tissue elasticity parameters found in the literature and clinical force values. The synthetic mammograms were generated by a mammogram acquisition model using a monoenergetic parallel beam approximation applied to the synthetically compressed breast phantom.
An automated system for detecting and classifying particular types of tumors in digitized mammograms is described. The analysis of mammograms is performed in two stages. First, the system identifies pixel groupings that may correspond to tumors. Next, detected pixel groupings are subjected to classification. The essence of the first processing stage is multiresolution image processing based on fuzzy pyramid linking. The second stage uses a classification hierarchy to identify benign and malignant tumors. Each level of the hierarchy uses deterministic or Bayes classifiers and a particular measurement. The measurements pertain to shape and intensity characteristics of particular types of tumors. The classification hierarchy is organized in such a way that the simplest measurements are used at the top, with the system stepping through the hierarchy only when it cannot classify the detected pixel groupings with certainty.
We have evaluated a method for synthesizing mammograms by comparing the texture of clinical and synthetic mammograms. The synthesis algorithm is based upon simulations of breast tissue and the mammographic imaging process. Mammogram texture was synthesized by projections of simulated adipose tissue compartments. It was hypothesized that the synthetic and clinical texture have similar properties, assuming that the mammogram texture reflects the 3D tissue distribution. The size of the projected compartments was computed by mathematical morphology. The texture energy and fractal dimension were also computed and analyzed in terms of the distribution of texture features within four different tissue regions in clinical and synthetic mammograms. Comparison of the cumulative distributions of the mean features computed from 95 mammograms showed that the synthetic images simulate the mean features of the texture of clinical mammograms. Correlation of clinical and synthetic texture feature histograms, averaged over all images, showed that the synthetic images can simulate the range of features seen over a large group of mammograms. The best agreement with clinical texture was achieved for simulated compartments with radii of 4-13.3 mm in predominantly adipose tissue regions, and radii of 2.7-5.33 and 1.3-2.7 mm in retroareolar and dense fibroglandular tissue regions, respectively.
A method is proposed for realistic simulation of the breast ductal network as part of a computer three-dimensional (3-D) breast phantom. The ductal network is simulated using tree models. Synthetic trees are generated based upon a description of ductal branching by ramification matrices (R matrices), whose elements represent the probabilities of branching at various levels of a tree. We simulated the ductal network of the breast, consisting of multiple lobes, by random binary trees (RBT). Each lobe extends from the ampulla and consists of branching ductal segments of decreasing size, and the associated terminal ductal-lobular units. The lobes follow curved paths that project from the nipple toward the chest wall. We have evaluated the RBT model by comparing manually-traced ductal networks from 25 projections of ductal lobes in clinical galactograms and manually-traced networks from 23 projections of synthetic RBTs. A root-mean-square (rms) fractional error of 41%, between the R-matrix elements corresponding to clinical and synthetic images, was computed. This difference was influenced by projection and segmentation artifacts and by the limited number of available images. In addition, we analyzed 23 synthetic trees generated using R matrices computed from clinical images. A comparison of these synthetic and clinical images yielded a rms fractional error of 11%, suggesting the possibility that a more appropriate model of the ductal branching morphology may be developed. Rejection of the RBT model also suggests the existence of a relationship between ductal branching morphology and the state of mammary developmentand pathology.
This paper describes part of a study aimed at developing a computer-based aid for mammogram screening that makes a detailed comparison between mammograms of the same patient acquired at different screenings and detects changes indicative of cancer. The focus is on determining control points in two mammograms; these points are used to put two mammograms into correspondence. The paper details the algorithm for identifying the potential control points and establishing the correspondence between the two sets of control points. The algorithm's performance was evaluated by three observers, one of whom is an experienced radiologist, and found to be adequate.
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