Modification of BM3D Algorithm for Representing Volumetric Data on Medical Images

Authors

  • Kamalakshi N r JSSRF, SJCE Campus Mysore, India
  • H Naganna Professor of Computer Science & Engg Bangalore, India
  • M N Shanmukhaswamy Professor of Electronics & Communication SJCE Mysore, India

Keywords:

Modified BM3D, Volumetric Data, Image reconstruction

Abstract

In the past decade, sufficient powerful Denoising algorithms have been devised - among them the patch-based nonlocal schemes, such as BM3D, have shown outstanding performance The BM3D employs a non-local modeling of images by collecting similar image patches in 3D arrays. The so-called collaborative ï¬ltering applied on such a 3D array is realized by transform-domain shrinkage. The block-matching with 3D transform domain collaborative filtering (BM3D) achieves very good performance in image Denoising. However, BM3D becomes ineffective when an image is heavily contaminated by noise. This is because it allows block-matching to search out of the region where a template block is located, resulting in poor matching. To address this, paper proposes an approach that is an extension of BM3D to represent to volumetric data & image reconstruction.

References

. Levin and B. Nadler, “Natural image Denoising: Optimality and inherent bounds,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2011

. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image denoising with block-matching and 3D filltering,î in Proc. SPIE Electronic Imaging: Algorithms and Systems V, vol. 6064A-30, San Jose, CA, USA,January 2006.

. J. V. Manjon, P. Coupe, L. Martı-Bonmat, D. L. Collins, and M. Robles, “Adaptive non-ocal means denoising of MR images with spatially varying noise levels,” Journal of Magnetic Resonance Imaging, vol. 31,pp. 192–203, 2010

. Milindkumar V. Sarode Prashant R. Deshmukh “Reduction of Speckle Noise and Image Enhancement of Images Using Filtering Technique” International Journal of Advancements Technology “ISSN 0976-4860 Vol 2, No 1

. A. Buades, B. Coll, and J. M. Morel, .A review of image denoising algorithms, with a new one,. Multiscale Modeling and Simulation, vol. 4, no. 2, pp. 490.530, 2005.

. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, .Image Denoising by sparse 3D transform-domain collaborative filtering,. IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080.2095, August 2007.

. M.C. Motwani, M.C. Gadiya and R.C. Motwani, "Survey of Image Denoising Techniques", proceedings of GSPx, Santa Clara, CA., Sep., 2004.

. A. Buades, B. Coll, and J Morel. On image Denoising methods. Technical Report 2004-15, CMLA, 2004.

. A. Buades, B. Coll, and J Morel. A non-local algorithm for image Denoising. IEEE International Conference on Computer Vision and Pattern Recognition, 2005.

. Color image Denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space in IEEE Int. Conf. Image Processing., San Antonio, Texas, September 2007,

. W. Evans. Image Denoising with the non-local means algorithm.

http://www.cs.wisc.edu/~evanswes/cs766.html

. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image Denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080-2095, August 2007

. P. Milanfar, “A tour of modern image filtering,” Invited feature article to IEEE Signal Processing Magazine (preprint at http:// users. soe. ucsc. edu/milanfar/ publications/ ), 2011.

. K. Egiazarian, A. Foi, and V. Katkovnik, “Compressed sensing image reconstruction via recursive spatially adaptive filtering,” in IEEE International Conference on Image Processing., vol. 1, October 2007, pp.549–552

. A. Danielyan, A. Foi, V. Katkovnik, and K. Egiazarian, “Spatially adaptive filtering as regularization in inverse imaging: compressive sensing, upsampling, and super-resolution,” in Super-Resolution Imaging. CRC Press / Taylor & Francis, 2010.

. R. Vincent, “Brainweb: Simulated brain database,” http://mouldy.bic.mni.mcgill.ca/brainweb/, 2006.

Downloads

Published

2013-12-30

How to Cite

[1]
Kamalakshi N, H Naganna, and M N Shanmukhaswamy, “Modification of BM3D Algorithm for Representing Volumetric Data on Medical Images”, Int. J. Comp. Sci. Eng., vol. 1, no. 4, pp. 11–17, Dec. 2013.

Issue

Vol. 1 No. 4 (2013): IJCSE December Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.