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On the Performance of Histogram Equalization Techniques in Enhancement of Proton Density Weighted Magnetic Resonance Images


Affiliations
1 Department of Mechatronics Engineering, KAMARAJ College of Engineering and Technology, K. Vellakulam, India
 

Magnetic Resonance Images (MRI) are used by Physician to analyse the body structures to find the diseases & to monitor the treatments. For effective analysis, they should consist of all relevant information in a better visualization format. However, MRI images suffer from poor dynamic range which affects the visible quality due to low contrast. Medical Image enhancement is a powerful tool to increase the perception of information to provide better diagnosis. In this study, different histogram equalization techniques like Global Histogram Equalization (GHE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE), Recursive Mean Separate Histogram Equalization (RMSHE), Brightness Preserving Dynamic Histogram Equalization (BPDHE), Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE)and Contrast-limited Adaptive Histogram Equalization (CLAHE) are applied to proton density weighted magnetic resonance image to enhance the contrast and their performance is compared in terms Discrete Entropy (DE), Measure of Enhancement (EME), Average Brightness (AB) and Pixel Distance (PD). Based on the performance metrics, the best histogram equalization technique in enhancing the contrast of PD weighted MRI images is determined.

Keywords

Average Brightness, Contrast Enhancement, Discrete Entropy, Histogram Equalization, Magnetic Resonance Images, Measure of Enhancement and Pixel Distance.
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  • David Pickens, “Handbook of Medical Imaging”, Volume 1. Physics and Psychophysics, https://doi.org/10.1117/3.832716.ch6.

  • https://www.imaios.com/en/e-Courses/e-MRI/MRIsignal- contrast/Signal-weighting

  • https://radiopaedia.org/articles/mri-sequences- overview

  • Gonzalez R.C., Woods R.E., “Digital Image processing”, second ed. Prentice Hall 2002.

  • Y. T. Kim, “Contrast Enhancement Using Brightness Preserving Bi-Histogram Equation”, IEEE Transactions on Consumer Electronics, vol. 43, no. 1, (1997) February, pp. 1-8.

  • Y. Wang, Q. Chen and B. Zhang, “Image Enhancement Based on Equal Area Dualistic Sub-Image Histogram Equalization Method”, IEEE Transactions on Consumer Electronics, vol. 45, no. 1, (1999), pp. 68-75.

  • S.D. Chen and A. R. Ramli, “Contrast Enhancement using Recursive Mean-Separate Histogram Equalization for Scalable Brightness Preservation”, IEEE Transactions on Consumer Electronics, vol. 49, no. 4, (2003) November, pp. 1301-1309.

  • H. Ibrahim and N. S. Pik Kong, “Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement”, IEEE Transactions on Consumer Electronics, vol. 53, no. 4, (2007) November, pp. 1752- 1758.

  • D. Sheet, H. Garud, A. Suveer, M. Mahadevappa and J. Chatterjee, “Brightness Preserving Dynamic Fuzzy Histogram Equalization”, IEEE Transactions on Consumer Electronics, vol. 56, no. 4, (2010) November, pp. 2475-2480.

  • Zuiderveld, Karel, “Contrast Limited Adaptive Histograph Equalization.” Graphic Gems IV. San Diego: Academic Press Professional, 474–485, 1994.

  • C. E. Shannon, "A mathematical theory of communication," Bell Syst. Tech. J., vol. 27, pp. 623- 656, Oct. 1948.

  • S. Again, B. Silver, and K. Panetta, "Transform coefficient histogram-based image enhancement algorithms using contrast entropy," IEEE Trans. Image Process., vol. 16, no. 3, pp. 741-758, Mar. 2007.

  • Z. Chen, B. R. Abidi, D. L. Page, and M. A. Abidi, "Gray-level grouping (GLG): An automatic method for optimized image contrast enhanceemnt-part I: The basic method," IEEE Trans. Image Process., vol. 15, no. 8, pp. 2290-2302, Aug. 2006.

  • https://data.lhncbc.nlm.nih.gov


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  • On the Performance of Histogram Equalization Techniques in Enhancement of Proton Density Weighted Magnetic Resonance Images

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Authors

K. Kannan
Department of Mechatronics Engineering, KAMARAJ College of Engineering and Technology, K. Vellakulam, India
S. Wesley Moses Samdoss
Department of Mechatronics Engineering, KAMARAJ College of Engineering and Technology, K. Vellakulam, India

Abstract


Magnetic Resonance Images (MRI) are used by Physician to analyse the body structures to find the diseases & to monitor the treatments. For effective analysis, they should consist of all relevant information in a better visualization format. However, MRI images suffer from poor dynamic range which affects the visible quality due to low contrast. Medical Image enhancement is a powerful tool to increase the perception of information to provide better diagnosis. In this study, different histogram equalization techniques like Global Histogram Equalization (GHE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE), Recursive Mean Separate Histogram Equalization (RMSHE), Brightness Preserving Dynamic Histogram Equalization (BPDHE), Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE)and Contrast-limited Adaptive Histogram Equalization (CLAHE) are applied to proton density weighted magnetic resonance image to enhance the contrast and their performance is compared in terms Discrete Entropy (DE), Measure of Enhancement (EME), Average Brightness (AB) and Pixel Distance (PD). Based on the performance metrics, the best histogram equalization technique in enhancing the contrast of PD weighted MRI images is determined.

Keywords


Average Brightness, Contrast Enhancement, Discrete Entropy, Histogram Equalization, Magnetic Resonance Images, Measure of Enhancement and Pixel Distance.

References