Multi-image CAD employing features derived from ipsilateral mammographic views

Walter F. Good; Bin Zheng; Yuan-Hsiang Chang; Xiao Hui Wang; Glenn S. Maitz; David Gur

doi:10.1117/12.348602

21 May 1999 Multi-image CAD employing features derived from ipsilateral mammographic views

Walter F. Good, Bin Zheng, Yuan-Hsiang Chang, Xiao Hui Wang, Glenn S. Maitz, David Gur

Proceedings Volume 3661, Medical Imaging 1999: Image Processing; (1999) https://doi.org/10.1117/12.348602
Event: Medical Imaging '99, 1999, San Diego, CA, United States

Abstract

On mammograms, certain kinds of features related to masses (e.g., location, texture, degree of spiculation, and integrated density difference) tend to be relatively invariant, or at last predictable, with respect to breast compression. Thus, ipsilateral pairs of mammograms may contain information not available from analyzing single views separately. To demonstrate the feasibility of incorporating multi-view features into CAD algorithm, `single-image' CAD was applied to each individual image in a set of 60 ipsilateral studies, after which all possible pairs of suspicious regions, consisting of one from each view, were formed. For these 402 pairs we defined and evaluated `multi-view' features such as: (1) relative position of centers of regions; (2) ratio of lengths of region projections parallel to nipple axis lines; (3) ratio of integrated contrast difference; (4) ratio of the sizes of the suspicious regions; and (5) measure of relative complexity of region boundaries. Each pair was identified as either a `true positive/true positive' (T) pair (i.e., two regions which are projections of the same actual mass), or as a falsely associated pair (F). Distributions for each feature were calculated. A Bayesian network was trained and tested to classify pairs of suspicious regions based exclusively on the multi-view features described above. Distributions for all features were significantly difference for T versus F pairs as indicated by likelihood ratios. Performance of the Bayesian network, which was measured by ROC analysis, indicates a significant ability to distinguish between T pairs and F pairs (A_z equals 0.82 +/- 0.03), using information that is attributed to the multi-view content. This study is the first demonstration that there is a significant amount of spatial information that can be derived from ipsilateral pairs of mammograms.

Citation Download Citation

Walter F. Good, Bin Zheng, Yuan-Hsiang Chang, Xiao Hui Wang, Glenn S. Maitz, and David Gur "Multi-image CAD employing features derived from ipsilateral mammographic views", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); https://doi.org/10.1117/12.348602

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