论文信息 - Analysis and synthesis of three-dimensional Gaussian Markov random fields

Analysis and synthesis of three-dimensional Gaussian Markov random fields

A method for synthesis of 3-D Gaussian Markov random fields (GMRF) is presented. Following this, a scheme for the estimation of the parameters of the model using the method of least squares (LS) and several fast techniques for segmentation of volumetric imaging are outlined. The superior performance of the 3-D analysis algorithms over 2-D processing slice by slice is shown using both synthetic textured images and real brain magnetic resonance (MR) images.

Elena Ranguelova | Anthony Quinn | A. Quinn | E. Ranguelova

[1] R. Chellappa. Two-Dimensional Discrete Gaussian Markov Random Field Models for Image Processing , 1989 .

[2] Anil K. Jain,et al. Random field models in image analysis , 1989 .

[3] Anthony Quinn,et al. Fast and fully unsupervised scheme for model-based image segmentation , 1998, Optics & Photonics.

[4] K. Mardia. Multi-dimensional multivariate Gaussian Markov random fields with application to image processing , 1988 .

[5] Ron Kikinis,et al. Markov random field segmentation of brain MR images , 1997, IEEE Transactions on Medical Imaging.

[6] Anthony Quinn,et al. A data-driven Bayesian sampling scheme for unsupervised image segmentation , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[7] Jongwon Kim,et al. Volumetric object reconstruction using the 3D-MRF model-based segmentation [magnetic resonance imaging] , 1997, IEEE Transactions on Medical Imaging.