Construction and validation of mean shape atlas templates for atlas-based brain image segmentation
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Model-Based Segmentation of Hippocampal Subfields in Ultra-High Resolution In Vivo MRI
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Semisupervised probabilistic clustering of brain MR images including prior clinical information
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
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We propose a unified framework in which atlas-based segmentation and non-rigid registration of the atlas and the study image are iteratively solved within a maximum-likelihood expectation maximization (ML-EM) algorithm. Both segmentation and registration processes minimize the same functional, i.e. the log-likelihood, with respect to classification parameters and the spatial transformation. We demonstrate how both processes can be integrated in a mathematically sound and elegant way and which advantages this implies for both segmentation and registration performance. This method (Extended EM, EEM) is evaluated for atlas-based segmentation of MR brain images on real data and compared to the standard EM segmentation algorithm without embedded registration component initialized with an affine registered atlas or after registering the atlas using a mutual information based non-rigid registration algorithm (II).