Hidden MRF model-based algorithms for NMR image analysis

Presents a new framework for unsupervised NMR image analysis based on hidden MRF modeling and algorithms. According to the NMR image statistics, two types of hidden MRF models are introduced and justified in terms of stochastic regularization. The image analysis is then formulated as an optimization problem and achieved in two stages: estimate the model parameters to initialize the maximum likelihood solution and conduct finer segmentation through Bayesian decisions using the local context. The solution of the new problem formulation is implemented with an efficient multistage procedure. The experimental results with real NMR images are provided to demonstrate the promise and effectiveness of the proposed technique.<<ETX>>

[1]  T. Lei,et al.  A new look at finite mixture models in medical image analysis , 1994, Proceedings of ICSIPNN '94. International Conference on Speech, Image Processing and Neural Networks.

[2]  Tianhu Lei,et al.  Statistical approach to X-ray CT imaging and its applications in image analysis. I. Statistical analysis of X-ray CT imaging , 1992, IEEE Trans. Medical Imaging.

[3]  Anil K. Jain,et al.  MRF model-based algorithms for image segmentation , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[4]  Tianhu Lei,et al.  Statistical analysis of MR imaging and its applications in image modeling , 1994, Proceedings of 1st International Conference on Image Processing.

[5]  Tianhu Lei,et al.  A New Stochastic Model-Based Image Segmentation Technique For X-Ray CT Image , 1988, Other Conferences.

[6]  King-Sun Fu,et al.  Handbook of pattern recognition and image processing , 1986 .

[7]  James W. Modestino,et al.  A model-fitting approach to cluster validation with application to stochastic model-based image segmentation , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[8]  Yue Joseph Wang,et al.  Detection of the number of image regions by minimum bias/variance criterion , 1994, Other Conferences.

[9]  Tianhu Lei,et al.  Tissue type detection by block processing , 1994, Medical Imaging.

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

[11]  Tulay Adali,et al.  Block-wise segmentation via vector quantization for medical image analysis , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.