Relax Your Mind

Relaxation Techniques

Get Instant Access

[1] Jain, A. K., Advances in mathematical models for image processing, Proc. IEEE, Vol. 69, No. 5, pp. 502-528, 1981.

[2] Chellapa, R. and Kashyap, R. L., Digital image restoration using spatial interaction models, IEEE Trans. Acoust., Speech, Signal Process., Vol. 30, No. 3, pp. 461-471, 1982.

[3] Besag, J. E., On the statistical analysis of dirty pictures, J. R. stat. Soc., London, B, Vol. B-48, pp. 259-302, 1986.

[4] Bouman, C. A. and Shapiro, M., A multiscale random field model for Bayessian image segmentation, IEEE Trans. Image Process., Vol. 3, No. 2, pp. 162-177, 1994.

[5] Benvensite, A., Nikoukhah, R., and Willsky, A. S., Multiscale system theory, In: Proc. 29th Conf. Decision and Control, Dec. 1990, pp. 24842489, 1990.

[6] Basseville, M., Benveniste, A., Chou, K. C., Golden, S. A., Nikoukhah, R., and Willsky, A. S., Modeling and estimation of multiresolution stochastic processes, IEEE Trans. Inform. Theory, Vol. 38, No. 2, pp. 766-784, 1992.

[7] Amit, Y., Grenander, U., and Piccioni, M., Structure image restoration through deformable templates, J. Am. Stat. Assoc., Vol. 86, No. 414, pp. 376-387, 1991.

[8] Kam, A. C. and Kopec, G. E., Heuristic image decoding using separable source models, In Proc. Int. Conf. Acoustic., Speech, Signal Processing, Vol. 5, Adelaide, Australia, Apr. 19-22, pp. 145-148, 1994.

[9] Kopec, G. E. and Chou, P. A., Document image decoding using Markov source models, IEEE Trans. Pattern Anal. Machine Intell, Vol. 16, No. 6, pp. 602-617, 1994.

[10] Jain, A. K. and Dubes, R. C., Random field models in image analysis, J. Appl. stat., Vol. 16, No. 2, 1989.

[11] Geman, S. and Geman, D., Stochastic relaxation, Gibbs distribution, and Baysian restoration of images, IEEE Trans. on Pattern Anal. Mach. Intell., Vol. PAMI, No. 6, pp. 721-741, 1984.

[12] Geman, S. and Geman, D., Stochastic relaxation, Gibbs distribution, and Bayesian restoration of images, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 6, No. 6, pp. 721-741, 1984.

[13] Besag, J. E., Spatial interaction and the statistical analysis of lattice system, J. R. Stat. Soc., London, B, Vol. 36, pp. 192-236, 1974.

[14] Serra, J., Image Analysis and Mathematical Morphology, Academic Press, New York, 1982.

[15] Derin, H. and Elliott, H., Modeling and segmentation of noisy and texture images using Gibbs random fields, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 9, pp. 39-55, 1987.

[16] Farag, A. A. and Delp, E. J., Image segmentation based on composite random field models, J. Opt. Eng., Vol. 12, pp. 2594-2607, 1992.

[17] Bouman, C. A. and Liu, B., Multiple resolution segmentation of textured images, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 13, pp. 99113, 1991.

[18] El-Baz, A. and Farag, A. A., Image segmentation using GMRF models: Parameters estimation and applications, IEEE International Conference on Image Processing, Barcelona, Spain, pp. 177-180, 2003.

[19] El-Baz, A. and Farag, A. A., Parameter estimation in Gibbs Markov image models, 6th international Conf. Information Fusion, Queensland, Australia, pp. 934-942, 2003.

[20] El-Baz, A. andFarag, A. A., Stochastic models in image analysis: Parameter estimations and case studies in image segmentation, IEEE Workshop on Statistical Signal Processing, St. Louis, Mo, USA pp. 154-157, 2003.

[21] Boiselle, P. M. and White, C. S., New Technique in Thoracic Imaging, Marcel Daker Inc., New York, 2002.

[22] Farag, A. A., El-Baz, A., and Gimel'farb G., Detection and recognition of lung abnormalities using deformable templates, Proc. IAPR Int. Conf. Pattern Recognition (ICPR 2004), Cambridge, UK, 2004.

[23] Hu, S. and Hoffman, E. A., Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images, IEEE Trans. Med. Imaging, Vol. 20, No. 6. pp. 490-498, 2001.

[24] Condon, B. R., Patterson, J., and Wyper, D., Image nonuniformity in magnetic resonance imaging: Its magnitude and methods for its correction, Br. J. Radiol., Vol. 60, pp. 83-87, 1987.

[25] McVeigh, E. R., Bronskil, M. J., and Henkelman, R. M., Phase and sensitivity of receiver coils in magnetic resonance imaging, Med. Phys., Vol. 13, pp. 806-814, 1986.

[26] Wicks, D. A. G., Barker, G. J., and Tofts, P. S., Correction of intensity nonuniformity in MR images of any orientation, Magn. Reson. Imaging, Vol. 11, pp. 183-196, 1993.

[27] Simmons, A., Tofts, P. S., Barker, G. J., and Arrdige, S. R., Sources of intensity nonuniformity in spin echo images at 1.5 T, Magn. Reson. Med., Vol. 32, pp. 121-128, 1994.

[28] Wells, W. M., III, Grimson, W. E. L., Kikinis, R., andJolesz, F. A., Adaptive segmentation of MRI data, IEEE Trans. Med. Imaging, Vol. 15, pp. 429442, 1996.

[29] Johnston, B., Atkins, M. S., Mackiewich, B., and Anderson, M., Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI, IEEE Trans. Med. Imaging, Vol. 15, No. 2, pp. 154-169, 1996.

[30] Sled, J. G., Zijdenbos, A. P., and Evans, A. C., A nonparametric method for automatic correction of intensity nonuniformity in MRI data, IEEE Trans. Med. Imaging, Vol. 17, No. 1, pp. 87-97, 1998.

[31] Lai, S. and Fang, M., A new variational shape-from-orientation approach to correcting intensity inhomogeneities in MR images, Workshop on Biomedical Image Analysis, CVPR98, Santa Barbara, CA, pp. 56-63, 1998.

[32] Meyer, C. R., Bland, P. H., and Pipe, J., Retrospective correction of intensity inhomogeneities in MRI, IEEE Trans. Med. Imaging, Vol. 14, No. 1, pp. 36-41, 1995.

[33] Tincher, M., Meyer, C. R., Gupta, R., and Williams, D. M., Polynomial modeling and reduction of RF body coil spatial inhomogeneity in MRI, IEEE Trans. Med. Imaging, Vol. 12, No. 2, pp. 361-365, 1993.

[34] Brinkmann, B. H., Manduca, A., and Robb, R. A., Optimized homomor-phic unsharp masking for MR grayscale inhomogeneity correction, IEEE Trans. Med. Imaging, Vol. 17, No. 2, pp. 161-171, 1998.

[35] Dawant, B., Zijidenbos, A., and Margolin, R., Correction of intensity variations in MR images for computer aided tissue classification, IEEE Trans. Med. Imaging, Vol. 12, pp. 770-781, 1993.

[36] Gilles, S., Brady, M., Declerck, J., Thirion, J. P., and Ayache, N., Bias field correction of breast MR images, Proceedings of the Fourth International Conference on Visualization in Biomedical Computing, Hamburg, Germany, pp. 153-158, 1996.

[37] Brechbuhler, C., Gerig, G., and Szekely, G., Compensation of spatial inhomogeneity in MRI based on a parametric bias estimate, Proceedings of the Fourth International Conference on Visualization in Biomedical Computing, Hamburg, Germany, pp. 141-146, 1996.

[38] Guillemaud, R. and Brady, M., Estimating the bias field of MR images, IEEE Trans. Med. Imaging, Vol. 16, No. 3, pp. 238-251, 1997.

[39] Bezdek, J. C. and Pal, S. K., Fuzzy models for pattern recognition, IEEE Press, Piscataway, NJ, 1991.

[40] Dunn, J. C., A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, J. Cybern., Vol. 3, pp. 3257, 1973.

[41] Bezdek, J., A convergence theorem for the fuzzy ISODATA clustering algorithms, IEEE Trans. Pattern Anal. Mach. Intell., 1980.

[42] Xu, C., Pham, D., and Prince, J., Finding the brain cortex using fuzzy segmentation, isosurfaces, and deformable surfaces, Proc. of the fifteenth Int. Conf. on Information Processing in Medical Imaging (IPMI 97), pp. 399-404, 1997.

[43] Pham, D. L. and Prince, J. L., Adaptive fuzzy segmentation of magnetic resonance image, IEEE Trans. Med. Imaging, Vol. 18, No. 9, pp. 737752, 1999.

[44] Ahmed, M. N., Yamany, S. M., Mohamed, N. A., Farag, A. A., and Mori-arty, T., Bias field estimation and adaptive segmentation of MRI data using modified fuzzy C-means algorithm, Proc. IEEE Int. Conf. Computer Vision and Pattern Recogn., Fort Collins, Co., Vol. 1, pp. 250-255, 1999.

[45] Udupa, J. K. and Samarasekera, S., Fuzzy connectedness and object definition: Theory, algorithm and applications in image segmentation, Graph. Models and Image Process., Vol. 58, No. 3, pp. 246-261, 1996.

[46] Yamany, S. M., Farag, A. A., and Hsu, S., A fuzzy hyperspectral classifier for automatic target recognition (ATR) systems, Pattern Recognit. Lett., Vol. 20, pp. 1431-1438, 1999.

[47] Bezdek, J., Hall, L., and Clarke, L., Review of MR image segmentation using pattern recognition, Med. Phy., Vol. 20, pp. 1033-1948, 1993.

[48] Brandt, M. E., Bohan, T. P., Kramer, L. A., and Fletcher, J. M., Estimation of CSF, white matter and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images, Comput. Med. Imaging Graph, Vol. 18, pp. 25-34, 1994.

[49] Hall, L. O., Bensaid, A. M., Clarke, L. P., Velthuizen, R. P., Silbiger, M. S., and Bezdek, J. C., A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain, IEEE Trans. Neural Net., Vol. 3, pp. 672-682, 1992.

[50] Mohamed, Nevin A., Modified Fuzzy C-Mean Algorithm for Medical Image Segmentation, M.Sc. Thesis, Elect. Eng. Dept., University of Louisville, 1999.

[51] Goldszal, A. F., Davatzikos, C., Pham, D. L., Yan, M. X. H., Bryan, R. N., and Resnick, S. M., An image processing system for qualitative and quantitative volumetric analysis of brain images, J. Comput. Assist. Tomogr, Vol. 22, No. 5, pp. 827-837, 1998.

[52] Sethian, J. A., Level Set Methods and Fast Marching Methods, Cambridge, 1999.

[53] Zaho, H-K., Chan, T., Merriman, B., and Osher, S., A variational level set approach to multiphase motion, J. Comput. Phys., Vol. 127, pp. 179-195, 1996.

[54] Hongchuan Yu, Dejun Wang, Zesheng Tang, Level set methods and image segmentation, IEEE International Workshop on Medical Imaging and Augmented Reality (MIAR'01), 2001.

[55] Sussman, M., Smereka, P., and Osher, S., A level set approach for computing solutions to incompressible two-phase flow, J. Comput. Phys., Vol. 114, pp. 146-159, 1994.

[56] Wilson, D. L. and Noble, J. A., An adaptive segmentation algorithm for time-of-flight MRA data, IEEE Trans. Med. Imaging, Vol. 18, No. 10, pp. 938-945, 1999.

[57] Cline, H. E., Lorensen, W. E., Kikinis, R., and Jolesz, R., Three-dimensional segmentation of MR images of the head using probability and connectivity, Neurosurgery, Vol. 14, pp. 1037-1045, 1990.

[58] Nakajima, S., Atsumi, H., and Bhalerao, A. H., Computer-assisted surgical planning for cerebrovascular neurosurgery, Neurosurgery, Vol. 41, pp. 403-409, 1997.

[59] Cline, H. E., Lorensen, W. E., Souza, S. P., Jolesz, F. A., Kikinis, R., Gerig, G., and Kennedy, T. E., 3D surface rendered MR images of the brain and its vasculature, J. Comput. Assist. Tomogr., Vol. 15, pp. 344351, 1991.

[60] Yim, Peter J., Choyke, Peter L., and Ronald M., Summers, Gray-scale skeletonization of small vessels in magnetic resonance angiography, IEEE Trans. Med. Imaging, Vol. 19, No. 6, pp. 568-576, 2000.

[61] Bullitt, E., Symbolic description of intracerebral vessels segmented from magnetic resonance angiograms and evaluation by comparison with X-ray angiograms, Med. Image Anal., Vol. 5, pp. 157-169, 2001.

[62] Hossam, H. and Farag, Aly A., MRA data segmentation using level sets, Proc. of the International Conference of Image Processing (ICIP), Barcelona, Spain, pp. 173-176, 2003.

[63] Mohamed Sabry, Sites, Charles B., Farag, Aly A., Stephen Hushek, and Thomas Moriarty, Statistical Cerebrovascular segmentation for phase-contrast MRA data, Proc. of the First International Conf. on Biomedical Engineering, Cairo, Egypt, 2002.

Was this article helpful?

0 0
Relaxation Audio Sounds Relaxation

Relaxation Audio Sounds Relaxation

This is an audio all about guiding you to relaxation. This is a Relaxation Audio Sounds with sounds called Relaxation.

Get My Free MP3 Audio

Post a comment