Computer-aided detection of breast masses in digital breast tomosynthesis (DBT): improvement of false positive reduction by optimization of object segmentation

Jun Wei; Heang-Ping Chan; Berkman Sahiner; Lubomir M. Hadjiiski; Mark A. Helvie; Chuan Zhou; Yao Lu

doi:10.1117/12.878214

15 March 2011 Computer-aided detection of breast masses in digital breast tomosynthesis (DBT): improvement of false positive reduction by optimization of object segmentation

Jun Wei, Heang-Ping Chan, Berkman Sahiner, Lubomir M. Hadjiiski, Mark A. Helvie, Chuan Zhou, Yao Lu

Author Affiliations +

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 796311 (2011) https://doi.org/10.1117/12.878214
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

DBT is a promising new imaging modality that may improve the sensitivity and specificity for breast cancer detection. However, DBT could only provide quasi-3D information with limited resolution along the depth (Z) direction because tomosynthesis only has limited angular information for reconstruction. Our purpose of this study is to develop a mass segmentation method for a computer-aided detection system in DBT. A data set of 50 two-view DBTs was collected with a GE prototype system. We reconstructed the DBTs using a simultaneous algebraic reconstruction technique (SART). Mass candidates including true and false masses were identified by 3D gradient field analysis. Two-stage 3D clustering followed by active contour segmentation was applied to a volume of interest (VOI) at each candidate location. We compared a fixed-Z approach, in which the Z dimension of the VOI was pre-determined, to an adaptive-Z approach, in which Z was determined by the object diameter (D) on the X-Y plane obtained from the first-stage clustering. We studied the effect of Z ranging from D to D+8 slices, centered at the central slice, in the second stage. Features were extracted on the individual slices of the segmented 3D object and averaged over all slices for both approaches. Linear discriminant analysis with stepwise feature selection was trained with a leave-one-case-out method to differentiate true from false masses in each feature space. With proper optimization of the adaptive-Z approach, the classification accuracy was significantly improved (p<0.0001) in comparison with the fixed-Z approach. The improved differentiation of true from false masses will be useful for false positive reduction in an automated mass detection system.

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Jun Wei, Heang-Ping Chan, Berkman Sahiner, Lubomir M. Hadjiiski, Mark A. Helvie, Chuan Zhou, and Yao Lu "Computer-aided detection of breast masses in digital breast tomosynthesis (DBT): improvement of false positive reduction by optimization of object segmentation", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796311 (15 March 2011); https://doi.org/10.1117/12.878214

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