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Proceedings Paper

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

Paper Details

Date Published: 8 March 2007
PDF: 8 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65122U (8 March 2007); doi: 10.1117/12.710198
Show Author Affiliations
Yi-Ta Wu, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Jiazheng Shi, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)
Chintana Paramagul, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)

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