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Proceedings Paper

Fully automated segmentation of whole breast in MR images by use of dynamic programming
Author(s): Luan Jiang; Yanyun Lian; Yajia Gu; Xiaoxin Hu; Qiang Li
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Paper Abstract

Breast segmentation is an important and challenging task for computerized analysis of background parenchymal enhancement (BPE) in dynamic contrast enhanced magnetic resonance images (DCE-MRI). The purpose of this study is to develop and evaluate a fully automated technique for accurate segmentation of whole breast in three-dimensional (3-D) DCE-MRI. The whole breast segmentation consists of two steps, i.e., the delineation of the chest wall and breast skin line. A sectional dynamic programming method was first designed in each 2-D slice to trace the upper and/or lower boundaries of the chest wall. The statistical distribution of gray levels of the breast skin line was employed as weighting factor to enhance the skin line, and dynamic programming was then applied to delineate breast skin line slice-by-slice within the automatically extracted volume of interest (VOI). Our method also took advantages of the continuity of chest wall and skin line across adjacent slices. Finally, the segmented breast skin line and the detected chest wall were connected to create the whole breast segmentation. The preliminary results on 70 cases show that the proposed method can obtain accurate segmentation of whole breast based on subjective observation. With the manually delineated region of 16 breasts in 8 cases, our method achieved Dice overlap measure of 92.1% ± 1.9% (mean ± SD) and volume agreement of 91.6% ± 4.7% for whole breast segmentation. It took approximately 4 minutes and 2.5 minutes for our method to segment the breast in an MR scan of 160 slices and 108 slices, respectively.

Paper Details

Date Published: 18 March 2014
PDF: 6 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90350W (18 March 2014); doi: 10.1117/12.2043343
Show Author Affiliations
Luan Jiang, Shanghai Advanced Research Institute (China)
Yanyun Lian, Shanghai Advanced Research Institute (China)
Yajia Gu, FuDan Univ. (China)
Xiaoxin Hu, Fudan Univ. (China)
Qiang Li, Shanghai Advanced Research Institute (China)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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