[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

New Contrast Enhancement Method for Multiple Sclerosis Lesion Detection

  • Original Paper
  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

Multiple sclerosis (MS) is one of the most serious neurological diseases. It is the most frequent reason of non-traumatic disability among young adults. MS is an autoimmune disease wherein the central nervous system wrongly destructs the myelin sheath surrounding and protecting axons of nerve cells of the brain and the spinal cord which results in presence of lesions called plaques. The damage of myelin sheath alters the normal transmission of nerve flow at the plaques level, consequently, a loss of communication between the brain and other organs. The consequence of this poor transmission of nerve impulses is the occurrence of various neurological symptoms. MS lesions cause mobility, vision, cognitive, and memory disorders. Indeed, early detection of lesions provides an accurate MS diagnosis. Consequently, and with the adequate treatment, clinicians will be able to deal effectively with the disease and reduce the number of relapses. Therefore, the use of magnetic resonance imaging (MRI) is primordial which is proven as the relevant imaging tool for early diagnosis of MS patients. But, low contrast MRI images can hide important objects in the image such lesions. In this paper, we propose a new automated contrast enhancement (CE) method to ameliorate the low contrast of MRI images for a better enhancement of MS lesions. This step is very important as it helps radiologists in confirming their diagnosis. The developed algorithm called BDS is based on Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) and Singular Value Decomposition with Discrete Wavelet Transform (SVD-DWT) techniques. BDS is dedicated to improve the low quality of MRI images with preservation of the brightness level and the edge details from degradation and without added artifacts or noise. These features are essential in CE approaches for a better lesion recognition. A modified version of BDS called MBDS is also implemented in the second part of this paper wherein we have proposed a new method for computing the correction factor. Indeed, with the use of the new correction factor, the entropy has been increased and the contrast is greatly enhanced. MBDS is specially dedicated for very low contrast MRI images. The experimental results proved the effectiveness of developed methods in improving low contrast of MRI images with preservation of brightness level and edge information. Moreover, performances of both proposed BDS and MBDS algorithms exceeded conventional CE methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al.: Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 17:162–73, 2017. https://doi.org/10.1016/S1474-4422(17)30470-2.

  2. McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD, et al.: Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol. 50:121–7, 2001. https://doi.org/10.1002/ana.1032.

    Article  CAS  PubMed  Google Scholar 

  3. Smith CM, Hale LA, Olson K, Baxter GD, Schneiders AG: Healthcare provider beliefs about exercise and fatigue in people with multiple sclerosis. J Rehabil Res Dev. 50(5):733-44, 2013. https://doi.org/10.1682/jrrd.2012.01.0012. PMID: 24013920.

    Article  PubMed  Google Scholar 

  4. Lezak MD, Howieson DB, Bigler ED, Tranel D: Neuropsychological Assessment. 5th ed. New-York : Oxford University Press. 2012.

    Google Scholar 

  5. Maor Y, Olmer L, Mozes B: The relation between objective and subjective impairment in cognitive function among multiple sclerosis patients-the role of depression. Mult Scler. 7(2):131-5, 2001. https://doi.org/10.1177/135245850100700209.

    Article  CAS  PubMed  Google Scholar 

  6. Politte LC, Huffman JC, Stern TA : Neuropsychiatric manifestations of multiple sclerosis. Prim Care Companion J Clin Psychiatry. 10(4):318-24, 2008. https://doi.org/10.4088/pcc.v10n0408.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Feinstein A: Multiple sclerosis and depression. Multiple Sclerosis Journal. 17(11), 1276-1281, 2011. https://doi.org/10.1177/1352458511417835.

    Article  PubMed  Google Scholar 

  8. Goldenberg MM: Multiple sclerosis review. P T. 37(3):175–84, 2012.

  9. Rolak LA: Multiple sclerosis: it's not the disease you thought it was. Clin Med Res. 1(1):57-60, 2003. https://doi.org/10.3121/cmr.1.1.57.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Lassmann H: Multiple Sclerosis Pathology. Cold Spring Harb Perspect Med. 1;8(3):a028936, 2018. https://doi.org/10.1101/cshperspect.a028936.

  11. Hegen H, Bsteh G, Berger T: 'No evidence of disease activity' - is it an appropriate surrogate in multiple sclerosis? Eur J Neurol. 25(9):1107-e101, 2018. https://doi.org/10.1111/ene.13669.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Rovira À, Wattjes MP: Gadolinium should always be used to assess disease activity in MS – No. Multiple Sclerosis Journal. 26(7):767-769, 2020. https://doi.org/10.1177/1352458520914819.

    Article  PubMed  Google Scholar 

  13. Garcia-Bournissen F, Shrim A, Koren G: Safety of gadolinium during pregnancy. Can Fam Physician. 52(3):309-10, 2006.

    PubMed  PubMed Central  Google Scholar 

  14. Do C, DeAguero J, Brearley A, Trejo X, Howard T, Escobar GP, Wagner B: Gadolinium-Based Contrast Agent Use, Their Safety, and Practice Evolution. Kidney360. 1(6):561–568, 2020. https://doi.org/10.34067/KID.0000272019.

  15. Wang J, Yuan Y, Guoxiang L: Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation. Advances in Mathematical Physics. 2022(2):1-11, 2022. https://doi.org/10.1155/2022/1982555.

    Article  Google Scholar 

  16. Wang W, Yuan X, Chen Zh, Wu X, Gao Z: Weak-Light Image Enhancement Method Based on Adaptive Local Gamma Transform and Color Compensation. Journal of Sensors, 2021:1-18, 2021. https://doi.org/10.1155/2021/5563698.

    Article  Google Scholar 

  17. Zhao Zh, Gao X: Image Contrast Enhancement Method Based on Nonlinear Space and Space Constraints. Wireless Communications and Mobile Computing. 2022:1-9, 2022. https://doi.org/10.1155/2022/2572523.

    Article  Google Scholar 

  18. Chen CM, Chen CC, Wu MC, Horng G, Wu HC, Hsueh SH, Ho HY: Automatic Contrast Enhancement of Brain MR Images Using Hierarchical Correlation Histogram Analysis. Journal of medical and biological engineering, 35(6):724–734, 2015. https://doi.org/10.1007/s40846-015-0096-6.

  19. Muniyappan S, Rajendran P: Contrast Enhancement of Medial Images through Adaptive Genetic Algorithm (AGA) over Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Multimedia Tools Appl. 78(6): 6487–6511, 2019. https://doi.org/10.1007/s11042-018-6355-0.

    Article  Google Scholar 

  20. Al-Ameen Z: Contrast Enhancement of Medical Images Using Statistical Methods with Image Processing Concepts. 6th International Engineering Conference “Sustainable Technology and Development" (IEC), 169–173, 2020. https://doi.org/10.1109/IEC49899.2020.9122925.

  21. Somasundaram K, Kalavathi P: Medical Image Contrast Enhancement based on Gamma Correction. International Journal of Knowledge Management and e-Learning, 3:15–18, 2011.

  22. Salem N, Malik H, Shams A: Medical image enhancement based on histogram algorithms. Procedia Computer Science, 163 :300-311, 2019. https://doi.org/10.1016/j.procs.2019.12.112.

    Article  Google Scholar 

  23. Kallel F, Sahnoun M, Ben Hamida A, Chtourou K: CT scan contrast enhancement using singular value decomposition and adaptive gamma correction. Signal, Image and Video Processing. 12:1-9, (2018). https://doi.org/10.1007/s11760-017-1232-2.

    Article  Google Scholar 

  24. Sahnoun M, Kallel F, Dammak M, Kammoun O, Mhiri CH, Ben Mahfoudh Kh, Ben Hamida A: Spinal cord MRI contrast enhancement using adaptive gamma correction for patient with multiple sclerosis. Signal, Image and Video Processing, 14(1):1-9, 2020. https://doi.org/10.1007/s11760-019-01561-x.

    Article  Google Scholar 

  25. Mnassri B, Echtioui A, Kallel F, Dammak M, Mhiri CH, Ben Hamida A: Image Enhancement Techniques Applied to Magnetic Resonance Images: Multiple sclerosis. 6th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). 1–5, 2022. https://doi.org/10.1109/ATSIP55956.2022.9805984.

  26. Subramani B, Veluchamy M: A fast and effective method for enhancement of contrast resolution properties in medical images. Multimedia Tools and Applications. 79(2), 2020. https://doi.org/10.1007/s11042-019-08521-0.

  27. Gonzalez RC, Woods RE: Digital image processing. 2nd Reading, MA. Addison-Wesley, 85–103, 1992.

  28. Ketcham DJ, Lowe RW, Weber JW: Image Enhancement Techniques for Cockpit Displays. Tech. rep., Hughes Aircraft, 1974. https://doi.org/10.21236/ada014928.

  29. Ketcham DJ, Lowe RW, Weber JW: Real-time image enhancement techniques. Seminar on Image Processing, Hughes Aircraft, 1–6, 1976. https://doi.org/10.1117/12.954708.

  30. Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, ter Haar Romeny BM, Zimmerman JB, Zuiderveld K: Adaptive Histogram Equalization and Its Variations. Comp. Vis., Graphics & Im. Proc. 39(3):355–368, 1987. https://doi.org/10.1016/S0734-189X(87)80186-X.

  31. Chai Hum Y, Kai Tee Y, Yap WSh, Mokayed H, Swee Tan T, Mohamad Salim MI, Wee Lai KH: A contrast enhancement framework under uncontrolled environments based on just noticeable difference. Signal Processing: Image Communication, 103, 2022. https://doi.org/10.1016/j.image.2022.116657.

  32. Rahman S, Rahman MM, Abdullah-Al-Wadud M et al. : An adaptive gamma correction for image enhancement. J Image Video Proc. 35 (2016), 2016. https://doi.org/10.1186/s13640-016-0138-1.

  33. Bhandari AK, Kumar A, Singh GK, Soni V: Dark satellite image enhancement using knee transfer function and gamma correction based on DWT-SVD. Multidimensional Syst. Signal Process. 27(2):453–476, 2016. https://doi.org/10.1007/s11045-014-0310-7.

    Article  Google Scholar 

  34. Wang CH, Ye ZH: Brightness preserving histogram equalization with maximum entropy: a variational perspective. IEEE Transactions on Consumer Electronics. 51(4):1326-1334, 2005. https://doi.org/10.1109/TCE.2005.1561863.

    Article  Google Scholar 

  35. Ibrahim H and Kong NS: Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement. IEEE Transactions on Consumer Electronics, 53(4), 2007. https://doi.org/10.1109/TCE.2007.4429280.

  36. Sheet D, Garud H, Suveer A, Mahadevappa M, Chatterjee J: Brightness preserving dynamic fuzzy histogram equalization. IEEE Transactions on Consumer Electronics. 56(4):2475 – 2480, 2010. https://doi.org/10.1109/TCE.2010.5681130.

    Article  Google Scholar 

  37. Demirel H, Anbarjafari G, Jahromi MNS: Image equalization based on singular value decomposition. 23rd IEEE International Symposium on Computer and Information Sciences, 1–5, 2008. https://doi.org/10.1109/ISCIS.2008.4717878.

  38. Demirel H, Ozcinar C, Anbarjafari G: Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. IEEE Geoscience and Remote Sensing Letters, 7(2):333–337, 2010. https://doi.org/10.1109/LGRS.2009.2034873.

  39. Bhandari AK, Kumar A, Padhy PK: Enhancement of low contrast satellite images using discrete cosine transform and singular value decomposition, World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 5(7):707–713, 2011. https://doi.org/10.5281/zenodo.1331359.

  40. Demirel H, Anbarjafari G: Image resolution enhancement by using discrete and stationary wavelet decomposition. IEEE Trans. Image Process. 20(5), 1458–1460, 2011. https://doi.org/10.1109/TIP.2010.2087767.

    Article  PubMed  Google Scholar 

  41. Loizou CP, Murray V, Pattichis MS, Seimenis I, Pantziaris M, Pattichis CS: Multi-scale amplitude modulation-frequency modulation (AM-FM) texture analysis of multiple sclerosis in brain MRI images. IEEE Transactions on Information Technology in Biomedicine. 15(1):119–129, 2011. https://doi.org/10.1109/TITB.2010.2091279.

  42. Loizou CP, Kyriacou EC, Seimenis I, Pantziaris M, Petroudi S, Karaolis M, Pattichis CS: Brain white matter lesion classification in multiple sclerosis subjects for the prognosis of future disability. Intelligent Decision Technologies Journal (IDT), 7:3–10, 2013. https://doi.org/10.3233/IDT-120147.

  43. Loizou CP, Pantziaris M, Pattichis CS, Seimenis I: Brain MRI Image normalization in texture analysis of multiple sclerosis. Journal of Biomedical Graphics and Computing, 3(1):20–34, 2013. https://doi.org/10.5430/jbgc.v3n1p20.

    Article  Google Scholar 

  44. Loizou CP, Petroudi S, Seimenis I, Pantziaris M, Pattichis CS: Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome. Journal of neuroradiology. 42(2): 99–114, 2015. https://doi.org/10.1016/j.neurad.2014.05.006.

  45. Atta R, Abdel-Kader RF: Brightness preserving based on singular value decomposition for image contrast enhancement. Optik, 126(7):799–803, 2015. https://doi.org/10.1016/j.ijleo.2015.02.025.

  46. Agaian SS, Silver B, Panetta KA: Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE transactions on image processing, 16(3), 741–758, 2007. https://doi.org/10.1109/tip.2006.888338.

  47. Horé A, Ziou D: Image Quality Metrics: PSNR vs. SSIM. 20th International Conference on Pattern Recognition, 2366–2369, 2010. https://doi.org/10.1109/ICPR.2010.579.

  48. Wang Z, Bovik AC, Sheikh HR, Simoncelli and EP: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600–612, 2004. https://doi.org/10.1109/TIP.2003.819861.

  49. Zhang L, Zhang L, Mou X, Zhang D: FSIM: A Feature Similarity Index for Image Quality Assessment. IEEE Transactions on Image Processing. 20(8):2378-2386, 2011. https://doi.org/10.1109/TIP.2011.2109730.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Laboratory of eHealth at the University of Cyprus (http://www.medinfo.cs.ucy.ac.cy/) and the Institute of Neurology and Genetics, at Nicosia, Cyprus and the Neurology and Radiology Department of UHC Habib Bourguiba, Sfax, Tunisia.

Author information

Authors and Affiliations

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

    Besma Mnassri, Amira Echtioui & Fathi Kallel

  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, Saudi Arabia

    Ahmed Ben Hamida

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

    Mariem Dammak & Chokri Mhiri

  5. Department of Radiology, CHU Habib Bourguiba, Sfax, Tunisia

    Kheireddine Ben Mahfoudh

Authors
  1. Besma Mnassri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amira Echtioui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fathi Kallel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed Ben Hamida
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mariem Dammak
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chokri Mhiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kheireddine Ben Mahfoudh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Besma Mnassri.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mnassri, B., Echtioui, A., Kallel, F. et al. New Contrast Enhancement Method for Multiple Sclerosis Lesion Detection. J Digit Imaging 36, 468–485 (2023). https://doi.org/10.1007/s10278-022-00729-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-022-00729-1

Keywords

Search

Navigation