17 March 2006 Mammographic CADx system using an image library with an intelligent agent: a pattern matching approach
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It is conceivable that a comprehensive clinical case library with intelligent agents can sort and render clinically similar cases and present clinically significant features to assist the radiologist in interpreting mammograms. In this study, we used a deformable vector diagram as the primary framework for matching the mammographic masses. The vector diagram provides gradient and shape features of the mass. The deformable algorithm allows flexible matching. The vector diagram was also incorporated with our newly developed delineation method using steepest changes of a probability based cost-function. Thus it allows us to automatically extract the main body and significant part of border region for pattern matching using a weighted mutual information technique. We have collected 86 mammograms. Of these cases, 46 contain a benign mass and the other 40 contain a malignant mass. Using the weighted mutual information technique on the vector diagram of the mass region, we found that the benign masses can be sorted into 6 groups except one case; the malignant masses can be sorted into 8 groups except two cases. For all 86 cases, the masses can be sorted into 13 groups except three cases. In addition, one group of benign masses and one group of malignant mass cases merged into one which contains 10 cases. Hence, the success sorting rate was 85.7% (12/14) in terms of group and was 84.9% (73/86) in terms of case, respectively.
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Shih-Chung B. Lo, Shih-Chung B. Lo, Matthew T. Freedman, Matthew T. Freedman, Lisa Kinnard, Lisa Kinnard, Erini Makariou, Erini Makariou, "Mammographic CADx system using an image library with an intelligent agent: a pattern matching approach", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61445O (17 March 2006); doi: 10.1117/12.654667; https://doi.org/10.1117/12.654667

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