Skip to main content
SpringerLink
Log in

Magnetic resonance imaging standardization for accurate grading of cerebral gliomas

  • 1181: Multimedia-based Healthcare Systems using Computational Intelligence
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Computer-aided diagnosis has attracted attention for the accurate grading of cerebral glioma. Most algorithms are only effective in relatively large datasets. Although multicenter data sharing is expanding, the results of cerebral glioma grading are not promising for multicenter data. Considering that multicenter images differ in contrast, we propose an effective image standardization method to reduce the disparity in image contrast of different datasets. The method is adopted in multiple sets of comparative experiments on a public dataset (BraTS2017) and a local dataset. The classification accuracy of experimental data relative to that of multicenter data without image normalization is improved by approximately 25% on average. Results demonstrate that the proposed approach is effective in solving the image contrast disparity of multicenter data. It also addresses the challenge of limited effective sample size in accurate cerebral glioma grading. The novel image standardization technology proposed in this work is a promising solution that can be integrated into expert systems.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Abdelaziz Ismael SA, Mohammed A, Hefny H (Jan 2020) An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Artif Intell Med 102:101779

    Article  Google Scholar 

  2. Ackaouy A, Courty N, Vallee E, Commowick O, Barillot C, Galassi F (2020) Unsupervised domain adaptation with optimal transport in multi-site segmentation of multiple sclerosis lesions from MRI data. Front Comput Neurosci 14:19

    Article  Google Scholar 

  3. S. Bakas et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features," Sci Data, vol. 4, p. 170117, Sep 5 2017.

  4. Bargshady G, Zhou X, Deo RC, Soar J, Whittaker F, Wang H (2020) Enhanced deep learning algorithm development to detect pain intensity from facial expression images. Expert Syst Appl 149:113305

    Article  Google Scholar 

  5. Celik T (2012) Two-dimensional histogram equalization and contrast enhancement. Pattern Recogn 45(10):3810–3824

    Article  Google Scholar 

  6. Chen SD, Ramli AR (2004) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans Consum Electron 49(4):1310–1319

    Article  Google Scholar 

  7. Chen X, Xie T, Fang J, Xue W, Tong H, Kang H, Wang S, Yang Y, Xu M, Zhang W (2017) Quantitative in vivo imaging of tissue factor expression in glioma using dynamic contrast-enhanced MRI derived parameters. Eur J Radiol 93:236–242

    Article  Google Scholar 

  8. Chen X et al (2019) Automatic histogram specification for Glioma grading using Multicenter data. J Healthc Eng:9414937

  9. Chen C, Dou Q, Chen H, Qin J, Heng PA (2020) Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE Trans Med Imaging 39(7):2494–2505

    Article  Google Scholar 

  10. B. H. Diplas et al., "Sensitive and rapid detection of TERT promoter and IDH mutations in diffuse gliomas," Neuro Oncol, vol. 21, no. 4, pp. 440–450, Mar 18 2019

  11. Ghassemi N, Shoeibi A, Rouhani M (2020) Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed Signal Process Control 57:101678

    Article  Google Scholar 

  12. Graham RN, Perriss RW, Scarsbrook AF (2005) DICOM demystified: a review of digital file formats and their use in radiological practice. Clin Radiol 60(11):1133–1140

    Article  Google Scholar 

  13. Han S, Liu Y, Cai SJ, Qian M, Ding J, Larion M, Gilbert MR, Yang C (2020) IDH mutation in glioma: molecular mechanisms and potential therapeutic targets. Br J Cancer 122(11):1580–1589

    Article  Google Scholar 

  14. Jin H, Luo Y, Li P, Mathew J (2019) A review of secure and privacy-preserving medical data sharing. IEEE Access 7:61656–61669

    Article  Google Scholar 

  15. Joseph J, Sivaraman J, Periyasamy R, Simi VR (2017) An objective method to identify optimum clip-limit and histogram specification of contrast limited adaptive histogram equalization for MR images. Biocybernet Biomed Eng 37(3):489–497

    Article  Google Scholar 

  16. Kudulaiti N, Qiu T, Lu J, Zhang H, Zhang Z, Guan Y, Zhuang D, Wu J (2019) Combination of magnetic resonance spectroscopy and 11C-methionine positron emission tomography for the accurate diagnosis of non-enhancing Supratentorial Glioma. Korean J Radiol 20(6):967–975

    Article  Google Scholar 

  17. Li W, Zhao Y, Chen X, Xiao Y, Qin Y (2019) Detecting Alzheimer's disease on small dataset: a knowledge transfer perspective. IEEE J Biomed Health Inform 23(3):1234–1242

    Article  Google Scholar 

  18. Li L, Wang K, Ma X, Liu Z, Wang S, du J, Tian K, Zhou X, wei W, Sun K, Lin Y, Wu Z, Tian J (2019) Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma. Eur J Radiol 118:81–87

    Article  Google Scholar 

  19. Liang D, Gao X, Lu W, He L (2020) Deep multi-label learning for image distortion identification. Signal Process 172:107536

    Article  Google Scholar 

  20. Li-Chun Hsieh K, Chen CY, Lo CM (2017) Quantitative glioma grading using transformed gray-scale invariant textures of MRI. Comput Biol Med 83:102–108

    Article  Google Scholar 

  21. Liu J, Chen F, Pan C, Zhu M, Zhang X, Zhang L, Liao H (2018) A cascaded deep convolutional neural network for joint segmentation and genotype prediction of brainstem Gliomas. IEEE Trans Biomed Eng 65(9):1943–1952

    Article  Google Scholar 

  22. Liu C, Sui X, Kuang X, Liu Y, Gu G, Chen Q (2019) Optimized contrast enhancement for infrared images based on global and local histogram specification. Remote Sens 11(7):849

  23. Liu Q, Jiang P, Jiang YH, Ge HJ, Li SL, Jin HW, Li YX (2019) Prediction of aneurysm stability using a machine learning model based on PyRadiomics-derived morphological features. Stroke 50(9):2314–2321

    Article  Google Scholar 

  24. Liu M, Zhou Z, Shang P, Xu D (2020) Fuzzified image enhancement for deep learning in Iris recognition. IEEE Trans Fuzzy Syst 28(1):92–99

    Article  Google Scholar 

  25. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 World Health Organization classification of Tumors of the central nervous system: a summary. Acta Neuropathol 131(6):803–820

    Article  Google Scholar 

  26. Lu Z, Bai Y, Chen Y, Su C, Lu S, Zhan T, Hong X, Wang S (2020) The classification of gliomas based on a pyramid dilated convolution resnet model. Pattern Recogn Lett 133:173–179

    Article  Google Scholar 

  27. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber MA, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp C, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin HC, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, van Leemput K (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993–2024

    Article  Google Scholar 

  28. Nyul LG, Udupa JK, Zhang X (2000) New variants of a method of MRI scale standardization. IEEE Trans Med Imag 19(2):143

    Article  Google Scholar 

  29. Patel V (2019) A framework for secure and decentralized sharing of medical imaging data via blockchain consensus. Health Informatics J 25(4):1398–1411

    Article  Google Scholar 

  30. Perone CS, Ballester P, Barros RC, Cohen-Adad J (2019) Unsupervised domain adaptation for medical imaging segmentation with self-ensembling. Neuroimage 194:1–11

    Article  Google Scholar 

  31. Raab P (2010) Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology 254(3):876–881

    Article  Google Scholar 

  32. Rao BS (2020) Dynamic histogram equalization for contrast enhancement for digital images. Appl Soft Comput 89:106114

    Article  Google Scholar 

  33. Roy R, Ghosh S, Ghosh A (2020) Clinical ultrasound image standardization using histogram specification. Comput Biol Med 120:103746

    Article  Google Scholar 

  34. Rundo L, Tangherloni A, Nobile MS, Militello C, Besozzi D, Mauri G, Cazzaniga P (2019) MedGA: a novel evolutionary method for image enhancement in medical imaging systems. Expert Syst Appl 119:387–399

    Article  Google Scholar 

  35. Sen D, Pal SK (May 2011) Automatic exact histogram specification for contrast enhancement and visual system based quantitative evaluation. IEEE Trans Image Process 20(5):1211–1220

    Article  MathSciNet  MATH  Google Scholar 

  36. Sepp M (2007) High-quality two-stage resampling for 3-D volumes in medical imaging. Med Image Anal 11(4):346–360

    Article  Google Scholar 

  37. van Griethuysen JJM et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104–e107

  38. Wachinger C, Reuter M (2016) Domain adaptation for Alzheimer's disease diagnostics. Neuroimage 139:470–479

    Article  Google Scholar 

  39. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  40. Wang R, Bao HB, du WZ, Chen XF, Liu HL, Han DY, Wang LG, Wu JN, Wang CL, Yang MC, Liu ZW, Zhang N, Teng L (Jan 2019) P68 RNA helicase promotes invasion of glioma cells through negatively regulating DUSP5. Cancer Sci 110(1):107–117

    Article  Google Scholar 

  41. Xie T, Chen X, Fang J, Kang H, Xue W, Tong H, Cao P, Wang S, Yang Y, Zhang W (2018) Textural features of dynamic contrast-enhanced MRI derived model-free and model-based parameter maps in glioma grading. J Magn Reson Imaging 47(4):1099–1111

    Article  Google Scholar 

  42. Xu G, Xu X, Wang X, Wang X (2019) Order-encoded quantum image model and parallel histogram specification. Quantum Information Process 18(11)

  43. Yoo JC, Ahn CW (2012) Image matching using peak signal-to-noise ratio-based occlusion detection. IET Image Process 6(5):483

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Engineering, Zhengzhou University, Zhengzhou, 450000, China

    Guohua Zhao & Yongcai Tao

  2. Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450004, China

    Guohua Zhao, Jie Bai & Jingliang Cheng

  3. Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, 450052, China

    Guohua Zhao & Yusong Lin

  4. School of Computer Science, Zhongyuan University of Technology, Zhengzhou, 450007, China

    Guan Yang

  5. School of Software, Zhengzhou University, Zhengzhou, 450002, China

    Lei Shi & Yusong Lin

  6. Hanwei IoT Institute, Zhengzhou University, Zhengzhou, 450002, China

    Yusong Lin

Authors
  1. Guohua Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yongcai Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jingliang Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yusong Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yusong Lin.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, G., Bai, J., Yang, G. et al. Magnetic resonance imaging standardization for accurate grading of cerebral gliomas. Multimed Tools Appl 81, 41477–41496 (2022). https://doi.org/10.1007/s11042-020-10487-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-10487-3

Keywords

Search

Navigation