HISTOGRAM EQUALIZATION BASED IMAGE ENHANCEMENT FOR MEDICAL IMAGE PROCESSING

Authors

  • Lavanya M Department of Electronics and Communication Engineering, IFET College of Engineering, Villupuram, Author

DOI:

https://doi.org/10.61841/b7yeay86

Keywords:

BPDHE algorithm, MRI image,, image enhancement

Abstract

In the medical field, resonance imaging (MRI) is one of the advanced techniques, which can be used to provide rich data regarding the human body. Tomography of the medical image may be a useful tool to help physicians to diagnose. Bar chart exploits are among the required steps within the image sweetening methods for Medical images. There are different ways of image sweetening, every one of them is required for a special sort of analysis. In this paper, Brightness preserving Dynamic Histogram Equalization (BPDHE) used for image enhancement. Contrast-enhanced is that the digital manipulating dispensed to increase excellence and reduces the noise in digital imaging.

 

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References

1. A. Laine, J. Fan, and W. Yang, “Wavelets for contrast enhancement of digital mammography,” IEEE Eng. Med. Biol. Mag., vol. 14, no. 6, pp. 536–550, 1995.

2. W. Qian, L. P. Clarke, B. Zheng, M. Kallergi, and R. Clark, “Computer assisted diagnosis for digital mammography,” IEEE Eng. Med. Biol. Mag., vol. 14, no. 6, pp. 561–568, 1995.

3. A. N. Netravali and B. G. Haskeli, Digital Pictures: Representation and Compression. New York: Plenum, 1988, ch. 4.

4. R. C. Gonzalez and R. E. Woods, Digital Image Processing. New York: Addison-Wesley, 1992. [5] M. A. Sid-Ahmed, Image Processing: Theory, Algorithms, and Architectures. New York: McGraw-Hill, 1995, ch. 4.

5. J. D. Fahnestock and R. A. Schowengerdt, “Spatially variant contrast enhancement using local range modification,” Opt. Eng., vol. 22, no. 3, pp. 378–381, 1983.

6. I. Altas, J. Louis, and J. Belward, “A variational approach to the radiometric enhancement of digital imagery,” IEEE Trans. Image Processing, vol. 4, pp. 845–849, June 1995.

7. V.Velusamy, Dr. M. Karnan, Dr. R. Sivakumar, Dr.N. Nandhagopal, “Enhancement Techniques and Methods for MRI A Review”, International Journal of Computer Science and Information Technologies, Vol. 5 (1), pp .397-403, 2014.

8. R. H. Sherrir and G. A. Johnson, “Regionally adaptive histogram equalization of the chest,” IEEE Trans. Med. Imag., vol. MI-6, pp. 1–7, Jan. 1987.

9. S. M. Pizer, J. B. Zimmerman, and E. V. Staab, “Adaptive grey level assignment in CT scan display,” J. Comput. Assist. Tomogr., vol. 8, no. 2, pp. 300–305, 1984.

10. S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. H. Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Comput. Vision, Graphics, Image Processing, vol. 39, pp. 355–368, 1987.

11. Md. Foisal Hossain, Mohammad Reza Alsharif, “Image Enhancement Based on Logarithmic Transform Coefficient and Adaptive Histogram Equalization”, 2007 International Conference on Convergence Information Technology, IEEE 2007.

12. K.Rajiv Gandhi, N.Nandhagopal, R.Sivasubramanian, “AutomaticSystem For Pre-Processing And Enhancement Of Magnetic Resonance Image (MRI)”,International Journal of Applied Engineering Research (IJAER)vol.9 (22),pp. 15485-15499,2014.

13. J. Alex Stark “Adaptive Image Contrast Enhancement Using Generalizations of Histogram Equalization”, IEEETransactions on ImageProcessing, Vol. 9, No. 5, May 2000.

14. Wang Yuanji. Li Jianhua, Lu E, Fu Yao, Jiang Qinzhong, “Image Quality Evaluation Based On Image Weighted Separating Block Peak Signal To Noise Ratio”, IEEE Int.Conf. Neural Networks &Signal Processing, Nanjing, China, December 14-17, 2003.

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Published

30.06.2020

How to Cite

M, L. (2020). HISTOGRAM EQUALIZATION BASED IMAGE ENHANCEMENT FOR MEDICAL IMAGE PROCESSING. International Journal of Psychosocial Rehabilitation, 24(4), 8900-8906. https://doi.org/10.61841/b7yeay86