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Edge Enhancement using Co-Occurrence Features of LBP Coded Low Contrast MR Images

Abraham Varghese, Balakrishnan Kannan

Abstract


Restoration of image features from local binary pattern (LBP) coded MR images form an important application in Telemedicine. LBP is invariant to monotonic gray scale change and it can be used as a rotational invariant texture descriptor. In this paper, we use co-occurrence features of local binary patterns to edge enhance low contrast MR images. It is thereby shown that the co-occurrence features are able to retrieve the anatomical information present in the original MR image, which otherwise can be obtained using a standard edge enhancing kernel. Out of 14 textural features extracted from co-occurrence matrix, the measure sum-average performs better in preserving anatomical information.

Keywords


Co-Occurrence Matrix, Edge Enhancement, Local Binary Pattern (LBP), Sum Average Haralick Feature (SAHF).

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References


Multi-scale Optics for Enhanced Light Collection from a Point Source Rachel Noek, Caleb Knoernschild,Justin Migacz, Taehyun Kim, 2010 Optical Society of America

Point Spread Function Optimization for MRI Reconstruction, Hao Tan and Yibin Zhen

B.R. Condon, J Patterson, et al.Image non uniformity in magnetic resonance imaging:its magnitude and method for correction”,The British Journal of Radiology, vol.60,pp.83- 87,198

Jeffrey C, Weinreb, Libby Brateman,“Chemical Shift Artefacts in Magnetic Resonance Images at 0.35T.AJR 145:183- 185, July 1985.

R. M. Haralick, K. Shanmugam, and I.Dinstein, Textural features for image classi¯cation, IEEE Transactions on Systems, Man, and Cybernetics 3, pp. 610-621, 1973.

C.R.Dyer, A.Rosenfeld, Fourier Texture Features: Suppression of Aperture Effects. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6 (October), 703-705, 1976.

I.Fogel, D.Sagi, Gabor Filters as Texture Discriminator. Biological Cybernetics, 61 (June), 103-113, 1989.

A.C Bovik, M.Clark, W.S.Geisler, Multichannel Texture Analysis Using Localized Spatial Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (January), 55-73, 1990.

T.R. Reed, H. Wechsler, Pattern analysis and Machine Intelligence 251 – 259.Segmentation of Textured Images and Gestalt Organization Using Spatial-Frequency Representations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (January), 1 – 12, 1990.

W. Y. Ma and B. S. Manjunath, A comparison of wavelet features for texture annotation, in Proceedings of the IEEE International Conference on Image Processing, 2, pp. 256-259, 1995.

J. Krum, S. A. Shafer, Texture segmentation and shape in the same image, Computer Vision, Proc. 5th International conference, 20-23 June 1995 Page (s):121– 127.

Robert M Haralick, K Shanmugam,(1973),"Textural Features for Image Classification". IEEE Transactions on Systems, Man, and Cybernetics SMC-3 (6): 610– 621

Davis, Larry S. Johns, Steven A. Aggarwal Texture Analysis Using Generalized Co-Occurrence Matrices, IEEE Transactions on image processing.

R. M. Haralick, Statistical and Structural Approaches to Texture, Proceedings of IEEE, Vol. 67, No. 5, pp. 786-809, 1979

A. Kak and C. Pavlopoulou, “Content-based image retrieval from large medical databases,” in Proc. 1st Int. Symp. 3D Data Process. Vis, Transmiss., Padova, Italy, 2002, pp. 138–147


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