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Dynamic multiple thresholding breast boundary detection algorithm for mammograms
Author(s) -
Wu YiTa,
Zhou Chuan,
Chan HeangPing,
Paramagul Chintana,
Hadjiiski Lubomir M.,
Daly Caroline Plowden,
Douglas Julie A.,
Zhang Yiheng,
Sahiner Berkman,
Shi Jiazheng,
Wei Jun
Publication year - 2010
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3273062
Subject(s) - thresholding , boundary (topology) , pixel , artificial intelligence , mammography , sobel operator , computer vision , mathematics , computer science , algorithm , image processing , edge detection , nuclear medicine , pattern recognition (psychology) , breast cancer , image (mathematics) , medicine , mathematical analysis , cancer
Purpose Automated detection of breast boundary is one of the fundamental steps for computer‐aided analysis of mammograms. In this study, the authors developed a new dynamic multiple thresholding based breast boundary (MTBB) detection method for digitized mammograms. Methods A large data set of 716 screen‐film mammograms (442 CC view and 274 MLO view) obtained from consecutive cases of an Institutional Review Board approved project were used. An experienced breast radiologist manually traced the breast boundary on each digitized image using a graphical interface to provide a reference standard. The initial breast boundary (MTBB‐Initial) was obtained by dynamically adapting the threshold to the gray level range in local regions of the breast periphery. The initial breast boundary was then refined by using gradient information from horizontal and vertical Sobel filtering to obtain the final breast boundary (MTBB‐Final). The accuracy of the breast boundary detection algorithm was evaluated by comparison with the reference standard using three performance metrics: The Hausdorff distance (HDist), the average minimum Euclidean distance (AMinDist), and the area overlap measure (AOM). Results In comparison with the authors’ previously developed gradient‐based breast boundary (GBB) algorithm, it was found that 68%, 85%, and 94% of images had HDist errors less than 6 pixels (4.8 mm) for GBB, MTBB‐Initial, and MTBB‐Final, respectively. 89%, 90%, and 96% of images had AMinDist errors less than 1.5 pixels (1.2 mm) for GBB, MTBB‐Initial, and MTBB‐Final, respectively. 96%, 98%, and 99% of images had AOM values larger than 0.9 for GBB, MTBB‐Initial, and MTBB‐Final, respectively. The improvement by the MTBB‐Final method was statistically significant for all the evaluation measures by the Wilcoxon signed rank test( p < 0 . 0001 ) . Conclusions The MTBB approach that combined dynamic multiple thresholding and gradient information provided better performance than the breast boundary detection algorithm that mainly used gradient information.