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A new denoising filter for brain MR images

Authors:

Ashish Phophalia,

Suman K. Mitra,

Ajit RajwadeAuthors Info & Claims

ICVGIP '12: Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing

Article No.: 57, Pages 1 - 8

https://doi.org/10.1145/2425333.2425390

Published: 16 December 2012 Publication History

Abstract

A new denoising filter is proposed for human brain MR image. The proposed filter is based on the notion of existing bilateral filter whose objective is to get a noise-free smooth image, preserving edges and other features intact. We have introduced a weighing function that controls the impact of existing bilateral filter for denoising. It is conditioned by Rough Edge Map (REM) and Rough Class Label (RCL). The presence of noise makes difficult to get precise information of edge and class label. Rough Set Technique is expected to assign rough (imprecise) class label and edge label to the pixels in the given image. This function thus is expected to handle the impreciseness of edge and class label and thereby preserving these two by controlling the bilateral filter more efficiently. The filter is extensively applied on brain MR images. The current proposal is compared with some of state-of-the-art approaches using different image quality measures and found to be efficient in most of the cases.

References

[1]

S. R. Arridge and A. Simmons. Multi-spectral probabilistic diffusion using bayesian classification. In Scale space theory in computer vision, pages 224--235, 1997.

Digital Library

[2]

S. M. Aswatha, J. Mukhopadhyay, and P. Bhowmick. Image denoising by scaled bilateral filter. In Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, pages 122--125, Dec. 2011.

Digital Library

[3]

M. Black, G. Sapiro, D. H. Marimont, and D. Heeger. Robust anisotropic diffusion. IEEE Trans. on Image Processing, 7(3): 421--432, March 1998.

Digital Library

[4]

D. L. Collins, A. Zijdenbos, V. Kollokian, J. Sled, N. Kabani, C. Holmes, and A. Evans. Design and construction of a realistic digital brain phantom. IEEE Trans. on Medical Imaging, 17(3): 463--468, June 1998.

[5]

R. Garnett, T. Huegerich, C. Chui, and W. He. A universal noise removal algorithm with an impulse detector. IEEE Trans. on Image Processing, 14(11): 1747--1754, November 2005.

Digital Library

[6]

A. E. Hassanien, A. Abraham, J. F. Peters, G. Schaefer, and C. Henry. Rough sets and near sets in medical imaging: A review. IEEE Trans. on Information Technology in Biomedicine, 13(6): 955--968, November 2009.

Digital Library

[7]

D. Malyszko and J. Stepaniuk. Adaptive multilevel rough entropy evolutionary thresholding. Information Sciences, 180: 1138--1158, 2010.

[8]

S. K. Mitra and G. L. Sicuranza. Nonlinear Image Processing. Academic Press, 2000.

Digital Library

[9]

P. Munshi and S. K. Mitra. A rough set based binarization technique for fingerprint images. In IEEE International Conference on Signal Processing Computing and Control, pages 1--6, March 2012.

[10]

S. K. Pal, B. U. Shankar, and P. Mitra. Granular computing, rough entropy and object extraction. Pattern Recognition Letters, 26: 2509--2517, 2005.

Digital Library

[11]

S. Patz and H. Gudbjartsson. The rician distribution of noisy mri data. Megn. Resonance Med., 34: 910--914, 1995.

[12]

Z. Pawlak and A. Skowron. Rudiments of rough sets. Information Sciences, 177: 3--27, 2007.

[13]

P. Perona and J. Malik. Scale space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Ana. Mach. Intell., 12(7): 629--639, July 1990.

Digital Library

[14]

V. B. S. Prasath and A. Singh. Edge detectors based anisotropic diffusion for enhancement of digital images. In Sixth Indian Conference on Computer Vision, Graphics and Image Processing, pages 33--38, 2008.

Digital Library

[15]

D. Sen and S. K. Pal. Histogram thresholding using beam theory and ambiguity measures. Fundam. Inf., 75(1--4): 483--504, 2007.

Digital Library

[16]

C. Tomasi and R. Manduchi. Bilateral filtering for gray and color images. In IEEE International Conference on Computer Vision, pages 839--846, 1998.

Digital Library

[17]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Trans. on Image Processing, 13(4): 600--612, Apr. 2004.

Digital Library

[18]

J. Weickart. Anisotropic Diffusion in Image Processing. B. G. Teubner Stuttgart, 1998.

[19]

W. C. K. Wong, A. C. S. Chung, and S. C. H. Yu. Trilateral filtering for biomedical images. In IEEE International Symposium on Biomedical Imaging: Nano to Macro, pages 820--823, March 2004.

[20]

L. Zhang, L. Zhang, X. Mou, and D. Zhang. Fsim: A feature similarity index for image quality assessment. IEEE Trans. on Image Processing, 20(9): 2378--2386, Aug. 2011.

Digital Library

Cited By

Phophalia AMitra S(2015)Rough set based bilateral filter design for denoising brain MR imagesApplied Soft Computing10.1016/j.asoc.2015.04.00533:C(1-14)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1016/j.asoc.2015.04.005
Phophalia ARajwade AMitra S(2014)Rough set based image denoising for brain MR imagesSignal Processing10.1016/j.sigpro.2014.01.029103:C(24-35)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1016/j.sigpro.2014.01.029
Phophalia AMitra SRajwade A(2013)Object boundary detection using Rough Set Theory2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)10.1109/NCVPRIPG.2013.6776259(1-4)Online publication date: Dec-2013
https://doi.org/10.1109/NCVPRIPG.2013.6776259
Show More Cited By

Index Terms

A new denoising filter for brain MR images
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Computer graphics
    1. Image manipulation

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Published In

cover image ACM Other conferences

ICVGIP '12: Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing

December 2012

633 pages

ISBN:9781450316606

DOI:10.1145/2425333

Program Chairs:
Bill Triggs
CNRS, France
,
Kavita Bala
Cornell University
,
Sharat Chandran
IIT Bombay, India

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 December 2012

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ICVGIP '12

ICVGIP '12: The Eighth Indian Conference on Vision, Graphics and Image Processing

December 16 - 19, 2012

Mumbai, India

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Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

Phophalia AMitra S(2015)Rough set based bilateral filter design for denoising brain MR imagesApplied Soft Computing10.1016/j.asoc.2015.04.00533:C(1-14)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1016/j.asoc.2015.04.005
Phophalia ARajwade AMitra S(2014)Rough set based image denoising for brain MR imagesSignal Processing10.1016/j.sigpro.2014.01.029103:C(24-35)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1016/j.sigpro.2014.01.029
Phophalia AMitra SRajwade A(2013)Object boundary detection using Rough Set Theory2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)10.1109/NCVPRIPG.2013.6776259(1-4)Online publication date: Dec-2013
https://doi.org/10.1109/NCVPRIPG.2013.6776259
Phophalia AMitra SRajwade A(2013)Medical image denoising from similar patches derived by Rough Set2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013)10.1109/ICIIP.2013.6707660(586-591)Online publication date: Dec-2013
https://doi.org/10.1109/ICIIP.2013.6707660

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