Recognition of lesion correspondence on two mammographic views: a new method of false-positive reduction for computerized mass detection

Berkman Sahiner; Nicholas Petrick; Heang-Ping Chan; Sophie Paquerault; Mark A. Helvie M.D.; Lubomir M. Hadjiiski

doi:10.1117/12.431139

3 July 2001 Recognition of lesion correspondence on two mammographic views: a new method of false-positive reduction for computerized mass detection

Berkman Sahiner, Nicholas Petrick, Heang-Ping Chan, Sophie Paquerault, Mark A. Helvie M.D., Lubomir M. Hadjiiski

Author Affiliations +

Proceedings Volume 4322, Medical Imaging 2001: Image Processing; (2001) https://doi.org/10.1117/12.431139
Event: Medical Imaging 2001, 2001, San Diego, CA, United States

Abstract

We used the correspondence of detected structures on two views of the same breast for false-positive (FP) reduction in computerized detection of mammographic masses. For each initially detected object on one view, we considered all possible pairings with objects on the other view that fell within a radial band defined by the nipple-to-object distances. We designed a 'correspondence classifier' to classify these pairs as either the same mass (a TP-TP pair) or a mismatch (a TP-FP, FP-TP or FP-FP pair). For each pair, similarity measures of morphological and texture features were derived and used as input features in the correspondence classifier. Two-view mammograms from 94 cases were used as a preliminary data set. Initial detection provided 6.3 FPs/image at 96% sensitivity. Further FP reduction in single view resulted in 1.9 FPs/image at 80% sensitivity and 1.1 FPs/image at 70% sensitivity. By combining single-view detection with the correspondence classifier, detection accuracy improved to 1.5 FPs/image at 80% sensitivity and 0.7 FPs/image at 70% sensitivity. Our preliminary results indicate that the correspondence of geometric, morphological, and textural features of a mass on two different views provides valuable additional information for reducing FPs.

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Berkman Sahiner, Nicholas Petrick, Heang-Ping Chan, Sophie Paquerault, Mark A. Helvie M.D., and Lubomir M. Hadjiiski "Recognition of lesion correspondence on two mammographic views: a new method of false-positive reduction for computerized mass detection", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.431139

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