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MRI contrast enhancement using singular value decomposition and brightness preserving dynamic fuzzy histogram equalization applied to multiple sclerosis patients

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Abstract

Multiple sclerosis (MS) is a neurological disease affecting the brain and spinal cord, which leads to several troubles such as numbness, memory problems, pain, fatigue and even paralysis. Magnetic resonance imaging (MRI) is commonly used for MS diagnosis. However, low-contrast MRI images need a contrast enhancement process to ameliorate the quality of images for better visualization of lesions. Nevertheless, most existing contrast enhancement methods add diverse types of alteration such as intensity modification, wash-out, noise and intensity saturation. Also, these methods change image brightness level. This paper presents a novel method for contrast improvement of low-contrast images referred to as BPDFHE-DWT-SVD. It is based on brightness preserving dynamic fuzzy histogram equalization (BPDFHE) and singular value decomposition with discrete wavelet transform (SVD-DWT) techniques. The aim behind the combination of those techniques is to enhance low-contrast images with preservation of the brightness level and without adding artifacts which is very important in lesion detection. To evaluate the performance of the new proposed method against existing contrast enhancement methods, different evaluation metrics are considered. Qualitative and quantitative analyses proved that the proposed method outperformed conventional methods. It enhances low-contrast MRI images with protection of brightness level and edge details from any distortion.

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References

  1. Do C., DeAguero J., Brearley A., Trejo X., Howard T., Escobar G.P., Wagner B.: Gadolinium-based contrast agent use, their safety, and practice evolution. Kidney360 1(6), 561–568 (2020)

    Article  Google Scholar 

  2. Rogosnitzky, M., Branch, S.: Gadolinium-based contrast agent toxicity: a review of known and proposed mechanisms. Biometals 29, 365–376 (2016)

    Article  Google Scholar 

  3. Vonghirandecha, P., Karnjanadecha, M., Intajag, S.: Contrast and color balance enhancement for non-uniform illumination retinal images. Tehnički Glasnik. 13(4), 291–296 (2019)

    Article  Google Scholar 

  4. Gonzalez, R.C., Woods, R.E.: Digital image processing. In: 2nd Reading, pp. 85–103. Addison-Wesley, MA (1992)

    Google Scholar 

  5. Narasimhan, K., Elamaran, V., Kumar, S., Sharma, K., Abhishek, P.R.: Comparison of satellite image enhancement techniques in wavelet domain. Res. J. Appl. Sci. Eng. Technol. 4(24), 5492–5496 (2012)

    Google Scholar 

  6. Goel, R.: The implementation of image enhancement techniques using Matlab. In: Proceedings of the International Conference on Innovative Computing & Communication (2021)

  7. Sheet, D., Garud, H., Suveer, A., Mahadevappa, M., Chatterjee, J.: Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans. Consum. Electron. 56(4), 2475–2480 (2010)

    Article  Google Scholar 

  8. Rahman, S., Rahman, M.M., Al Wadud, M.A., Al Quaderi, G.D., Shoyaib, M.: An adaptive gamma correction for image enhancement. EURASIP J. IVP 35, 2–13 (2016)

    Google Scholar 

  9. Fei, R., Weng, Y., Zhang, Y. and Lund, J.: Curve based fast detail enhancement for biomedical images. In: Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. 4, pp. 337–344 (2021)

  10. Sahnoun, M., Kallel, F., Dammak, M., Kammoun, O., Mhiri, C.H., Ben Mahfoudh, Kh., Ben Hamida, A.: spinal cord MRI contrast enhancement using adaptive gamma correction for patient with multiple sclerosis. Signal, Image Video Process 14, 377–385 (2020)

    Article  Google Scholar 

  11. Zhou, Y., Shi, Ch., Lai, B., Jimenez, G.: 2019 Contrast enhancement of medical images using a new version of the world cup optimization algorithm. Quantit. Imag. Med. Surg. 9(9), 1528–1547 (2019)

    Article  Google Scholar 

  12. Kallel, F., Sahnoun, M., Ben Hamida, A., Chtourou, K.: CT scan contrast enhancement using singular value decomposition and adaptive gamma correction. SIViP 12(3), 1–9 (2018)

    Google Scholar 

  13. Tarika, B., Kaur, M., Singh, I.: Review of histogram equalization techniques. Int. J. Adv. Trend. Comp. Sci. Eng. 3(11), 9205–9210 (2014)

    Google Scholar 

  14. Ketcham, D.J., Lowe R.W., and Weber, J.W.: Real-time image enhancement techniques. In: Seminar on Image Processing, Hughes Aircraft, pp. 1–6 (1976)

  15. Pizer, S.M.: Intensity mappings for the display of medical images. In: Functional Mapping of Organ Systems and Other Computer Topics, Society of Nuclear Medicine (1981)

  16. Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Haar Romeny, B., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Computer Vision, Graphics, Image Process. 39, 355–368 (1987)

    Article  Google Scholar 

  17. Kim, Y.T.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43, 1–8 (1997)

    Article  Google Scholar 

  18. Chen, S., Ramli, A.R.: Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans. Consum. Electron. 49, 1310–1319 (2003)

    Article  Google Scholar 

  19. Wang, C., Ye, Zh.: Brightness preserving histogram equalization with maximum entropy: a variational perspective. IEEE Trans. Consum. Electron. 51(4), 1326–1334 (2005)

    Article  Google Scholar 

  20. Abdullah-Al-Wadud, M., Kabir, H., Dewan, M.A., Chae, O.: A dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consum. Electron. 53(2), 593–600 (2007)

    Article  Google Scholar 

  21. Ibrahim, H., Kong, N.S.P.: Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consum. Electron. 53, 1752–1758 (2007)

    Article  Google Scholar 

  22. Demirel, H., Ozcinar, C., Anbarjafari, G.: Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. IEEE Geosci. Remote Sens. Lett. 7, 333–337 (2010)

    Article  Google Scholar 

  23. Demirel, H., Anbarjafari, G.: Image resolution enhancement by using discrete and stationary wavelet decomposition. IEEE Trans. Image Process. 20(5), 1458–1460 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  24. Kuber, P.S., Dixit, M., Silakari, S.: Improving brightness using dynamic fuzzy histogram equalization. Int. J. Sign. Process., Image Process. Patt. Recognit. 8(2), 303–312 (2015)

    Google Scholar 

  25. Bhandari, A.K., Kumar, A.K., Singh, G.K., Soni, V.: Dark satellite image enhancement using knee transfer function and gamma correction based on dwt-svd. Multidimen. Syst. Signal Process. 27(2), 453–476 (2016)

    Article  Google Scholar 

  26. Kallel, F., Ben Hamida, A.: A new adaptive gamma correction based algorithm using DWT-SVD for non-contrast CT image enhancement. IEEE Trans. Nanobiosci. 16(8), 666–675 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Advanced Technologies for Medicine and Signals Laboratory ‘ATMS’, National Engineering School of Sfax, Sfax University, Sfax, Tunisia

    Besma Mnassri, Fathi Kallel & Amira Echtioui

  2. National School of Electronics and Communications, Sfax University, Sfax, Tunisia

    Fathi Kallel

  3. Department IS, College of Computer Science, King Khalid University ‘KKU’, Abha, Kingdom of Saudi Arabia

    Ahmed Ben Hamida

  4. Neurology Department, CHU Habib Bourguiba, Sfax, Tunisia

    Mariem Dammak & Chokri Mhiri

Authors
  1. Besma Mnassri
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  2. Fathi Kallel
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  3. Amira Echtioui
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  4. Ahmed Ben Hamida
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  5. Mariem Dammak
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  6. Chokri Mhiri
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Correspondence to Besma Mnassri.

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Mnassri, B., Kallel, F., Echtioui, A. et al. MRI contrast enhancement using singular value decomposition and brightness preserving dynamic fuzzy histogram equalization applied to multiple sclerosis patients. SIViP 17, 2035–2043 (2023). https://doi.org/10.1007/s11760-022-02416-8

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  • DOI: https://doi.org/10.1007/s11760-022-02416-8

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