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Analysis of Background Detection and Contrast Enhancement of MRI Images

by S. Sathya, R. Manavalan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 36 - Number 12
Year of Publication: 2011
Authors: S. Sathya, R. Manavalan
10.5120/4548-6436

S. Sathya, R. Manavalan . Analysis of Background Detection and Contrast Enhancement of MRI Images. International Journal of Computer Applications. 36, 12 ( December 2011), 16-21. DOI=10.5120/4548-6436

@article{ 10.5120/4548-6436,
author = { S. Sathya, R. Manavalan },
title = { Analysis of Background Detection and Contrast Enhancement of MRI Images },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 36 },
number = { 12 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 16-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume36/number12/4548-6436/ },
doi = { 10.5120/4548-6436 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:23:01.876261+05:30
%A S. Sathya
%A R. Manavalan
%T Analysis of Background Detection and Contrast Enhancement of MRI Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 36
%N 12
%P 16-21
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a new method for poor lighting contrast enhancement of MRI images based on the Weber’s law. Background of MRI images is identified by using the contrast enhancement transformations. The contrast image transformation can be defined by two operators: opening and closing. The first operator employs information from block analysis, while the second transformation utilizes the opening by reconstruction. Opening by reconstruction is used to define the multi background notion. The objective of contrast operators is normalizing the grey level of an input MRI image by using the contrast operator. The normalization process will enhance the quality of MRI images by avoiding abrupt changes in intensity among different regions.

References
  1. F. Meyer and J. Serra, “Contrast and Activity Lattice,” Signal Process, vol. 16, pp. 303–317, 1989.
  2. I.R.Terol-Villalobos, “Morphological connected contrast mappings based on top-hat criteria: A multi scale contrast approach,” Opt. Eng., vol. 43, no. 7, pp. 1577–1595, 2004.
  3. A. Toet, “Multi scale contrast enhancement with applications to image fusion,” Opt. Eng., vol. 31, no. 5, 1992.
  4. J. Kasperek, “Real time morphological image contrast enhancement in virtex FPGA,” in Lecture Notes in Computer Science. New York: Springer, 2004.
  5. A. K. Jain, Fundamentals of Digital Images Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.
  6. C. R. González and E.Woods, Digital Image Processing. Englewood Cliffs, NJ: Prentice Hall, 1992.
  7. J. Serra, Mathematical Morphology Vol. I. London, U.K.: Academic, 1982.
  8. P. Soille, Morphological Image Analysis: Principle and Applications. New York: Springer-Verlag, 2003.
  9. H. Heijmans, Morphological Image Operators. New York: Academic, 1994.
  10. L. Vincent and E. R. Dougherty, “Morphological segmentation for textures and particles,” in Digital Image Processing Methods, E. R. Dougherty, Ed. New York: Marcel Dekker, 1994, pp. 43–102.
  11. L. Vincent, “Morphological gray scale reconstruction in image analysis: Applications and efficient algorithms,” IEEE Trans. Image Process., vol. 2, no. 2, pp. 176–201, Feb. 1993.
  12. E. Peli, “Contrast in complex images,” J. Opt. Soc. Amer., vol. 7, no. 10, pp. 2032–2040, 1990.
  13. A. Al-Manea and A. El-Zaart “Contrast enhancement of MRI images”. 2007.
  14. K.R.Ananth and Dr. S.Pannerselvam “Automated CAD for Nodule Detection for Magnetic Resonance Image ContrastEnhancement”. 2011
  15. GUO Huiling, XU Lan, On The Legal Mechanism of the Forestland Resources Protection in China, in: Forest Resources Management, June 2007,
  16. Perona, Malik, Scale space and edge detection using anisotropic di?usion, in: IEEE Trans Pattern Anal Machine Intell, 1990, (12), pp. 629-639
  17. FU Shujun, RUAN Qiuqi, MU Chengpo, WANG Wenqia, Edge detection for image measurement based on nonlinear di?usion ?lterin, in: Optics and Precision Engineering, Feb 2007,15(2).
  18. Osher,S.Rudin,L.I, Feature-oriented image enhancement using shock lters, in: SIAM Journal of Numerical Analysis, 1990, 27(4), http://citeseer.ist.psu.edu/context/116610/0
  19. Luis Alvarez, L.Mazor ra, Signal and Image Restoration using Shock Filters and Anisotropic Di?usion, in: SIAM Journal of Numer ical Analysis, 1994, 31(2), pp.590-605
  20. D.Tschumperle, R.Deriche, Vector image restoration using PDE, in: Proceedings of RFIA 2000, pp.247-255
  21. Wang Xiaofeng, Geng Guohua, Guo Hongbo, an improved image enhancement algorithm and its applcation, in: Computer Applications and Software, 2008, 25(9), pp.39-40
  22. Gilboa G, Sochen N, Zeevi YY, Forward and backward di?usion processes for adaptive image enhancement and denoising, in: IEEE Transaction on Image Processing, 2002, 11(7), pp.689-704
  23. Luis Alvare, Pierre-Louis Lions, Jean-Michel Morel, Image selsctive smoothing and edgedetection by nonlinear di?usion, in: SIAM J. NUMER. ANALJune 1992, 29(3), pp.845-866
Index Terms

Computer Science
Information Sciences

Keywords

Image background Morphological contrast Morphological filters by reconstruction Multi background Weber’s law

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