Skip to main content


Showing posts from January, 2017

3D Medical Image Segmentation

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