Super-resolved multi-channel fuzzy segmentation of MR brain images

We propose a new fuzzy segmentation framework that incorporates the idea of super-resolution image reconstruction. The new framework is designed to segment data sets comprised of orthogonally acquired magnetic resonance (MR) images by taking into account their different system point spread functions. Formulating the reconstruction within the segmentation framework improves its robustness and stability, and makes it possible to incorporate multispectral scans that possess different contrast properties into the super-resolution reconstruction process. Our method has been tested on both simulated and real 3D MR brain data.

[1]  Jerry L Prince,et al.  Optimization of MR pulse sequences for Bayesian image segmentation. , 1995, Medical physics.

[2]  Isabelle Bloch,et al.  Modeling anisotropic undersampling of magnetic resonance angiographies and reconstruction of a high-resolution isotropic volume using half-quadratic regularization techniques , 2004, Signal Process..

[3]  Scott T. Grafton,et al.  Automated image registration: I. General methods and intrasubject, intramodality validation. , 1998, Journal of computer assisted tomography.

[4]  Xiao Han,et al.  CRUISE: Cortical reconstruction using implicit surface evolution , 2004, NeuroImage.

[5]  A. Evans,et al.  MRI simulation-based evaluation of image-processing and classification methods , 1999, IEEE Transactions on Medical Imaging.

[6]  Jerry L Prince,et al.  Super-resolution Reconstruction of MR Brain Images , 2004 .

[7]  S. Resnick,et al.  One-year age changes in MRI brain volumes in older adults. , 2000, Cerebral cortex.

[8]  Dzung L. Pham,et al.  Robust fuzzy segmentation of magnetic resonance images , 2001, Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001.

[9]  Kevin J. Parker,et al.  MRI isotropic resolution reconstruction from two orthogonal scans , 2001, SPIE Medical Imaging.

[10]  Hayit Greenspan,et al.  MRI inter-slice reconstruction using super-resolution , 2002 .

[11]  Jerry L. Prince,et al.  Adaptive fuzzy segmentation of magnetic resonance images , 1999, IEEE Transactions on Medical Imaging.