This work is based on a Maximum a Posteriori (MAP) estimate of a log-likelihood function that accounts for the shape priori, the spatial interaction, and the current appearance of the tissues and its background (surrounding anatomical structures). Illustration of the Joint Markov-Gibbs random field (MGRF) image model Main references: Novel stochastic framework for accurate segmentation of prostate in dynamic contrast enhanced MRI International Workshop on Prostate Cancer Imaging, 121-130 A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Interested in pattern recognition and machine learning.