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Proceedings Paper

Automatic identification of pectoralis muscle on digital cranio-caudal-view mammograms
Author(s): Mei Ge; Gordon Mawdsley; Martin Yaffe
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Paper Abstract

To improve efficiency and reduce human error in the computerized calculation of volumetric breast density, we have developed an automatic identification process which suppresses the projected region of the pectoralis muscle on digital CC-view mammograms. The pixels in the image of the pectoralis muscle, represent dense tissue, but not related to risk, will cause an error in estimated breast density if counted as fibroglandular tissue. The pectoralis muscle on the CC-view is not always visible and has variable shape and location. Our algorithm robustly detects the existence of the pectoralis in the image and segments it as a semi-elliptical region that closely matches manually segmented images. We present a pipeline where adaptive thresholding and distance transforms have been used in the initial pectoralis region identification process; statistical region growing is applied to explore the region within the identified location aimed at refining the boundary; and a 2D shape descriptor is developed for the target validation: the segmented region is identified as the pectoralis muscle if it has a semi-elliptical contour. After the pectoralis muscle is identified, a 1D-FFT filtering is used for boundary smoothing. Quantitative evaluation was performed by comparing manual segmentation by a trained operator, and analysis using the algorithm in a set of 174 randomly selected digital mammograms. Use of the algorithm is shown to improve accuracy in the automatic determination of the volumetric ratio of breast composition by removal of the pectoralis muscle from both the numerator and denominator. As well, it greatly improves the efficiency and throughput in large scale volumetric mammographic density studies where previously interaction with an operator was required to obtain that level of accuracy.

Paper Details

Date Published: 8 March 2011
PDF: 8 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79631W (8 March 2011); doi: 10.1117/12.878785
Show Author Affiliations
Mei Ge, Sunnybrook Health Sciences Ctr. (Canada)
Gordon Mawdsley, Sunnybrook Health Sciences Ctr. (Canada)
Martin Yaffe, Sunnybrook Health Sciences Ctr. (Canada)
The Univ of Toronto (Canada)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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