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Enhancement of MRI images using modified type-2 fuzzy set

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Abstract

One of the most challenging, interesting, and influential areas in image processing is image enhancement. Image enhancement techniques manipulate the existing image so as to ameliorate the quality as well as the visual appearance of the image to the viewer. Different types of image enhancement methods are utilized to tackle the complex problems of image visualization in medical imaging. Many imaging techniques are available, such as CT scans, magnetic resonance imaging, X-rays, and others. MRI is a kind of scan that uses strong magnetic fields and radio waves to capture images of the internal structure of the patient’s body. Medical imaging is an exceptionally normal and fundamental medium for clinical experts to conclude illnesses with respect to unseen regions inside the body. In many situations, these images suffer from low contrast and bad illumination. To overcome these problems of low contrast and poor illumination, this paper presents an enhancement scheme using a modified type-2 fuzzy set for MRI images. The results of the proposed scheme are shown in terms of both qualitative and quantitative analysis. All the experiments are carried out for a fixed value of a parameter \(\beta =0.7\). For qualitative analysis, results are visualized with state-of-the-art methods and for quantitative analysis, PSNR, SSIM, AMBE, REC and PL are used. Qualitative and quantitative analysis bear witness to the fact that the performance of the proposed scheme is better in many places in comparison to other existing methods.

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Data Availability

On reasonable request, the dataset as well as the program utilised for this study will be made available to the reader.

References

  1. Agaian SS, Panetta K, Grigoryan AM (2001) Transform-based image enhancement algorithms with performance measure. IEEE Trans Image Process 10(3):367–382. https://doi.org/10.1109/83.908502

    Article  Google Scholar 

  2. Bai X, Zhou F, Xue B (2012) Image enhancement using multi scale image features extracted by top-hat transform. Opt Laser Technol 44(2):328–336. https://doi.org/10.1016/j.optlastec.2011.07.009

    Article  Google Scholar 

  3. Chaira T, (2012) Medical image enhancement using intuitionistic fuzzy set. In: (2012) 1st International conference on recent advances in information technology (RAIT), pp 54–57. IEEE. https://doi.org/10.1109/RAIT.2012.6194479

  4. Chaira T (2014) An improved medical image enhancement scheme using type ii fuzzy set. Appl Soft Comput 25:293–308. https://doi.org/10.1016/j.asoc.2014.09.004

    Article  Google Scholar 

  5. Dombi J (1982) A general class of fuzzy operators, the demorgan class of fuzzy operators and fuzziness measures induced by fuzzy operators. Fuzzy Sets Syst 8(2):149–163. https://doi.org/10.1016/0165-0114(82)90005-7

    Article  Google Scholar 

  6. Ensafi P, Tizhoosh HR (2005) Type-2 fuzzy image enhancement. In: International conference on image analysis and recognition, pp 159–166. Springer. https://doi.org/10.1007/11559573_20

  7. Fu X, Wang J, Zeng D, Huang Y, Ding X (2015) Remote sensing image enhancement using regularized-histogram equalization and dct. IEEE Geosci Remote Sens Lett 12(11):2301–2305. https://doi.org/10.1109/LGRS.2015.2473164

    Article  Google Scholar 

  8. Greenspan H, Anderson CH, Akber S (2000) Image enhancement by nonlinear extrapolation in frequency space. IEEE Trans Image Process 9(6):1035–1048. https://doi.org/10.1109/83.846246

    Article  MathSciNet  Google Scholar 

  9. Hanmandlu M, Jha D (2006) An optimal fuzzy system for color image enhancement. IEEE Trans Image Process 15(10):2956–2966. https://doi.org/10.1109/TIP.2006.877499

    Article  Google Scholar 

  10. Hasikin K, Isa NAM (2014) Adaptive fuzzy contrast factor enhancement technique for low contrast and nonuniform illumination images. Signal, Image and Video Process 8(8):1591–1603. https://doi.org/10.1007/s11760-012-0398-x

    Article  Google Scholar 

  11. Hemalatha S, Anouncia SM (2018) G-L fractional differential operator modified using auto-correlation function: texture enhancement in images. Ain Shams Eng J 9(4):1689–1704. https://doi.org/10.1016/j.asej.2016.12.003

    Article  Google Scholar 

  12. Hua M, Bie X, Zhang M, Wang W (2014) Edge-aware gradient domain optimization framework for image filtering by local propagation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2838–2845. https://doi.org/10.1109/CVPR.2014.363

  13. Ibrahim H, Kong NSP (2007) Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 53(4):1752–1758. https://doi.org/10.1109/TCE.2007.4429280

    Article  Google Scholar 

  14. Jenifer S, Parasuraman S, Kadirvelu A (2016) Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm. Appl Soft Comput 42:167–177. https://doi.org/10.1016/j.asoc.2016.01.039

    Article  Google Scholar 

  15. Kaur T, Sidhu RK (2015) Performance evaluation of fuzzy and histogram based color image enhancement. Procedia Comput Sci 58:470–477. https://doi.org/10.1016/j.procs.2015.08.009

    Article  Google Scholar 

  16. Kim TK, Paik JK, Kang BS (1998) Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Trans Consum Electron 44(1):82–87. https://doi.org/10.1109/30.663733

    Article  Google Scholar 

  17. Li B, Xie W (2015) Adaptive fractional differential approach and its application to medical image enhancement. Comput Electr Eng 45:324–335. https://doi.org/10.1016/j.compeleceng.2015.02.013

    Article  Google Scholar 

  18. Li G, Tong Y, Xiao X (2011) Adaptive fuzzy enhancement algorithm of surface image based on local discrimination via grey entropy. Procedia Eng 15:1590–1594. https://doi.org/10.1016/j.proeng.2011.08.296

    Article  Google Scholar 

  19. Ooi CH, Isa NAM (2010) Quadrants dynamic histogram equalization for contrast enhancement. IEEE Trans Consum Electron 56(4):2552–2559. https://doi.org/10.1109/TCE.2010.5681140

    Article  Google Scholar 

  20. Ooi CH, Kong NSP, Ibrahim H (2009) Bi-histogram equalization with a plateau limit for digital image enhancement. IEEE Trans Consum Electron 55(4):2072–2080. https://doi.org/10.1109/TCE.2009.5373771

    Article  Google Scholar 

  21. Paris S, Hasinoff SW, Kautz J (2011) Local laplacian filters: edge-aware image processing with a laplacian pyramid. ACM Trans Graph 30(4):1–12. https://doi.org/10.1145/2010324.1964963

    Article  Google Scholar 

  22. Puniani S, Arora, S (2016) Improved fuzzy image enhancement using l* a* b* color space and edge preservation. In: Advances in intelligent systems and computing (AISC), pp 459–469. Springer. https://doi.org/10.1007/978-3-319-23036-8_40

  23. Singh G, Mittal A (2014) Various image enhancement techniques-a critical review. Int J Innov Sci 10(2):267–274. https://www.issr-journals.org/xplore/ijisr/0010/002/IJISR-14-227-01.pdf

  24. Wadhwa A, Bhardwaj A (2021) Contrast enhancement of MRI images using morphological transforms and PSO. Multimed Tools Appl 80(14):21,595–21,613. https://doi.org/10.1007/s11042-021-10743-0

  25. Weber S (1983) A general concept of fuzzy connectives, negations and implications based on t-norms and t-conorms. Fuzzy Sets Syst 11(1–3):115–134. https://doi.org/10.1016/S0165-0114(83)80073-6

    Article  MathSciNet  Google Scholar 

  26. Yager RR (1980) On a general class of fuzzy connectives. Fuzzy Sets Syst 4(3):235–242. https://doi.org/10.1016/0165-0114(80)90013-5

    Article  MathSciNet  Google Scholar 

  27. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-i. Inf Sci 8(3):199–249. https://doi.org/10.1016/0020-0255(75)90036-5

    Article  MathSciNet  Google Scholar 

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Acknowledgements

The authors would like to thank the anonymous referees for their insightful comments, which significantly enhanced the quality of the manuscript. We are also grateful to the Vrinda Diagnostic Centre in Ghaziabad, India, for providing the image dataset.

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  1. Jaypee Institute of Information Technology, Noida, 201309, India

    Anjali Wadhwa & Anuj Bhardwaj

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  1. Anjali Wadhwa
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Correspondence to Anuj Bhardwaj.

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Wadhwa, A., Bhardwaj, A. Enhancement of MRI images using modified type-2 fuzzy set. Multimed Tools Appl 83, 75445–75460 (2024). https://doi.org/10.1007/s11042-024-18569-2

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