A dynamic multiple thresholding method for automated breast boundary detection in digitized mammograms

Yi-Ta Wu; Chuan Zhou; Lubomir M. Hadjiiski; Jiazheng Shi; Jun Wei; Chintana Paramagul; Berkman Sahiner; Heang-Ping Chan

doi:10.1117/12.710198

8 March 2007 A dynamic multiple thresholding method for automated breast boundary detection in digitized mammograms

Yi-Ta Wu, Chuan Zhou, Lubomir M. Hadjiiski, Jiazheng Shi, Jun Wei, Chintana Paramagul, Berkman Sahiner, Heang-Ping Chan

Author Affiliations +

Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 65122U (2007) https://doi.org/10.1117/12.710198
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

We have previously developed a breast boundary detection method by using a gradient-based method to search for the breast boundary (GBB). In this study, we developed a new dynamic multiple thresholding based breast boundary detection system (MTBB). The initial breast boundary (MTBB-Initial) is obtained based on the analysis of multiple thresholds on the image. The final breast boundary (MTBB-Final) is obtained based on the initial breast boundary and the gradient information from horizontal and the vertical Sobel filtering. In this way, it is possible to accurately segment the breast area from the background region. The accuracy of the breast boundary detection algorithm was evaluated by comparison with an experienced radiologist's manual segmentation using three performance metrics: the Hausdorff distance (HDist), the average minimum Euclidean distance (AMinDist), and the area overlap (AOM). It was found that 68%, 85%, and 90% of images have HDist errors less than 6 mm for GBB, MTBB-Initial, and MTBB-Final, respectively. Ninety-five percent, 96%, and 97% of the images have AMinDist errors less than 1.5 mm for GBB, MTBB-Initial, and MTBB-Final, respectively. Ninety-six percent, 97%, and 99% of the images have AOM values larger than 0.9 for GBB, MTBB-Initial, and MTBB-Final, respectively. It was found that the performance of the proposed method was improved in comparison to our previous method.

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Yi-Ta Wu, Chuan Zhou, Lubomir M. Hadjiiski, Jiazheng Shi, Jun Wei, Chintana Paramagul, Berkman Sahiner, and Heang-Ping Chan "A dynamic multiple thresholding method for automated breast boundary detection in digitized mammograms", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65122U (8 March 2007); https://doi.org/10.1117/12.710198

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