A Statistical Framework for Partial Volume Segmentation
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Improved tissue segmentation by including an MR acquisition model
MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis
A rough set-based magnetic resonance imaging partial volume detection system
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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Partial volume effects are present in nearly all-medical imaging data. These artifacts blur the boundaries between different regions, making accurate delineation of anatomical structures difficult. In this paper, we propose a method for unsupervised estimation of partial volume effects in single-channel image data. Based on a statistical image model, an algorithm is derived for estimating both partial volumes and the means of the different tissue classes in the image. To compensate for the ill-posed nature of the estimation problem, we employ a Bayesian approach that places a prior probability model on the parameters. We demonstrate on simulated and real images that the new algorithm is superior in several respects to the fuzzy and Gaussian clustering algorithms that have previously been used for modeling partial volume effects.