Skip to main content

Posts

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).





 Main references:
Novel stochastic framework for accurate segmentation of prostate in dynamic contrast enhanced MRI International Workshop on Prostate Cancer Imaging, 121-130A new 3D automatic segmentation framework for accurate segmentation of prostate from DCE-MRI 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro