25 November 2014 Segmenting pectoralis muscle on digital mammograms by a Markov random field-maximum a posteriori model
Author Affiliations +
Abstract
Accurate and automatic segmentation of the pectoralis muscle is essential in many breast image processing procedures, for example, in the computation of volumetric breast density from digital mammograms. Its segmentation is a difficult task due to the heterogeneity of the region, neighborhood complexities, and shape variability. The segmentation is achieved by pixel classification through a Markov random field (MRF) image model. Using the image intensity feature as observable data and local spatial information as
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Mei Ge, James G. Mainprize, Gordon E. Mawdsley, Martin J. Yaffe, "Segmenting pectoralis muscle on digital mammograms by a Markov random field-maximum a posteriori model," Journal of Medical Imaging 1(3), 034503 (25 November 2014). https://doi.org/10.1117/1.JMI.1.3.034503 . Submission:
JOURNAL ARTICLE
10 PAGES


SHARE
Back to Top