@inproceedings{923141f3a0f943de9cf209809855be1c,
title = "Brain tissue segmentation in PET-CT images using probabilistic atlas and variational Bayes inference",
abstract = "PET-CT provides aligned anatomical (CT) and functional (PET) images in a single scan, and has the potential to improve brain PET image segmentation, which can in turn improve quantitative clinical analyses. We propose a statistical segmentation algorithm that incorporates the prior anatomical knowledge represented by probabilistic brain atlas into the variational Bayes inference to delineate gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in brain PET-CT images. Our approach adds an additional novel aspect by allowing voxels to have variable and adaptive prior probabilities of belonging to each class. We compared our algorithm to the segmentation approaches implemented in the expectation maximization segmentation (EMS) and statistical parametric mapping (SPM8) packages in 26 clinical cases. The results show that our algorithm improves the accuracy of brain PET-CT image segmentation.",
keywords = "Brain image segmentation, Gaussian mixed model, PET-CT imaging, variational Bayes inference",
author = "Yong Xia and Jiabin Wang and Stefan Eberl and Michael Fulham and Feng, \{David Dagan\}",
year = "2011",
doi = "10.1109/IEMBS.2011.6091965",
language = "英语",
isbn = "9781424441211",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
pages = "7969--7972",
booktitle = "33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011",
note = "33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 ; Conference date: 30-08-2011 Through 03-09-2011",
}