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Design and Analysis of Fuzzy Algorithms for Medical Diagnosis

C. Vijayalakshmi, R. Kokila

Abstract


There has been increased interest in the development of fuzzy pattern recognition based medical imaging that contributes to solve the problems in early diagnosis and prognosis. Fuzzy set theory plays a key role in formalizing uncertainties for medical diagnosis and prognosis. This paper deals with some of the algorithmic approaches of fuzzy techniques

Keywords


Fuzzy Pattern, Diagnosis, Algorithmic Approach, Telemetric, Network

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References


Alirezaie J. and Jernigan ME., “MR Image Segmentation and Analysis Based on Neural Networks,” in Signal Processing for Magnetic Resonance Imaging and Spectroscopy, Marcel Dekker, pp. 341-364, 2002.

Atkins M. and Mackiewich B., “Fully Automatic Segmentation of the brain in MRI,” in Proceedings of IEEE Transaction, vol. 17.

Bedell B. and Narayana P., “Automatic Segmentation of Gadolinium enhanced Multiple Sclerosis Lesions,” Magnetic Resonance in Medicine, vol. 39, pp. 935-940, 1998.

Bezdek J., “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum Press, New York, 1981.

Brain W., “Simulated Brain Database,” McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill, Canada,.

Buxton R., “Introduction to Functional Magnetic Resonance Imaging-Principles and Techniques,” Cambridge University Press, 2002.

Chakeres D. and Schmalbrock P., Fundamentals of Magnetic Resonance Imaging, Williams and Wilkins, 1992.

Ng, H.P. Ong, S.H, Foong, K.W.C, Nowinski, W.L. (2005): “An improved watershed algorithm for medical image segmentation”, Proceedings 12th International Conference on Biomedical Engineering.

J.B.Antiine and Max A.Viergever, “A Survey of Medical Image Registration”, Oxford University, October16, 1997.

Dzung L.Pham, Chenyang Xu and Jerry L.Prince, “A Survey of Current Methods in Medical Image Segmentation”, Submitted for Publication to Annual Review of Biomedical Engineering, January, 19, 1998.

J. Meunier and M. Bertrand, “Ultrasonic texture motion analysis: Theory and Simulation,” IEEE Trans. Med. Imaging, vol. 14, no. 2, pp. 293-300, June 1995.

B. Julesz, “Texton gradients: The texton theory revisited,” Biol. Cybern., vol. 54, pp.245-251, 1986.

R. M. Haralick, “Statistical and structural approaches to texture,” Proc. IEEE, vol. 67, pp. 786-804, 1979.

Poularikas, A. D. (editor),. The Transforms and Applications Handbook. CRC Press and IEEE Press, 1996.

M. Unser, “Texture classification and segmentation using wavelet frames,” IEEE Trans. Image Processing, vol. 4, no. 11, pp. 1549-1560, Nov. 1995.

S. Mallat, “A theory of multiresolution signal decomposition: The wavelet representation,”, IEE Trans. Patt. Anal. Machine Intell., vol. 11, no. 7, pp. 674-693, Jul. 1989.

A.Rao “A taxonomy for texture description and identification”, Springer-Verlag, New York, 1990


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