Skip to main content

Skewness and Kurtosis of Apparent Diffusion Coefficient in Human Brain Lesions to Distinguish Benign and Malignant Using MRI

  • Conference paper
  • First Online:
Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Abstract

The application of Diffusion Weighted Imaging (DWI) in cancer identification and discrimination is increase singly interest within last decade. DWI has significant advantages, as it does not require contrast medium and provides qualitative and quantitative information that can be helpful for lesion assessment. Therefore, this study presents the utility of skewness and kurtosis of Apparent Diffusion Coefficient (ADC) to distinguish between benign and malignant brain lesions. All the Magnetic Resonance Imaging (MRI) scans were performed with a 3 Tesla Siemens Skyra MR system using a head coil. The sample consists of six subjects with locally advanced brain lesion. The Echo-Planar Imaging pulse sequence was used to acquire axial DW MRI data with a flip angle = \(90^{\circ }\), Time of Echo/Time of Repetition (TE/TR) = 98/6400 ms, Field of View (FOV) = 256 mm, matrix size = 256 \(\times \) 256, slice thickness of 1 mm and two levels of diffusion sensitization (\({\text {b} = 0 \text { and } 1000\,\text {s}/\text {mm}^2}\)). MATLAB 2014 Simulink software was used for the data analysis. The Region of Interest (ROI) the brain lesion was selected. The mean values of both the skewness and kurtosis of ADC within the ROI were determined and finally, the values were compared between benign and malignant brain lesions. The mean kurtosis and skewness of malignant and benign are 3.201, 3.738 and 0.071, 0.463 respectively. The mean kurtosis of benign is significantly high whereas mean skewness is significantly low. Therefore, there is a possibility of utilizing mean skewness and kurtosis pixel values as a potential biomarker to differentiate between benign and malignant brain lesions. ...

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Yousef, A., Elkharbotly, A., Settin, M., Mousa, Y.: Role of diffusion-weighted MR imaging in discrimination between the intracranial cystic masses. Egypt. J. Radiol. Nucl. Med. 45(3), 869–875 (2014)

    Article  Google Scholar 

  2. Aronen, H., et al.: Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 191(1), 41–51 (1994)

    Article  Google Scholar 

  3. Krabbe, K., Gideon, P., Wagn, P., Hansen, U., Thomsen, C., Madsen, F.: MR diffusion imaging of human intracranial tumours. Neuroradiology 39(7), 483–489 (1997)

    Article  Google Scholar 

  4. Provenzale, J.M., Mukundan, S., Baroriak, D.P.: Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment. Radiology 239(3), 632–649 (2006)

    Article  Google Scholar 

  5. Karaali, K., Bayrak, A.: Diffusion-weighted MRI: role in the differential diagnosis of the brain tumors. J. Cancer Prevent. Curr. Res. 2 (2015)

    Google Scholar 

  6. Cho, Y., Choi, G., Lee, S., Kim, J.: 1H-MRS metabolic patterns for distinguishing between meningiomas and other brain tumors. Magn. Reson. Imag. 21(6), 663–672 (2003)

    Article  Google Scholar 

  7. Wang, Q., Liacouras, E., Miranda, E., Kanamalla, U., Megalooikonomou, V.: Classification of brain tumors using MRI and MRS data. In: Medical Imaging 2007: Computer-Aided Diagnosis, vol. 6514, p. 65140S (2007)

    Google Scholar 

  8. Weber, M., et al.: Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors. Neurology 66(12), 1899–1906 (2006)

    Article  Google Scholar 

  9. Higano, S., et al.: Malignant astrocytic tumors: clinical importance of apparent diffusion coefficient in prediction of grade and prognosis. Radiology 241(3), 839–846 (2006)

    Article  Google Scholar 

  10. Kono, K., et al.: The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am. J. Neuroradiol. 22(6), 1081–1088 (2001)

    Google Scholar 

  11. Stephan, E.M., Sun, Y., Mulkern, R.V.: Diffusion imaging of brain tumors. NMR Biomed. 23(7), 849–864 (2010)

    Article  Google Scholar 

  12. Vermoolen, M., Kwee, T., Nievelstein, R.: Apparent diffusion coefficient measurements in the differentiation between benign and malignant lesions: a systematic review. Insights Imaging 3(4), 395–409 (2012)

    Article  Google Scholar 

  13. Allam, K.E., Shalaby, M.H., Moulood, I.A.: Role of diffusion weighted MRI imaging in detection of liver metakstases. Egypt. J. Hosp. Med. 69(2), 1823–1827 (2017)

    Article  Google Scholar 

  14. Rumboldt, Z., Camacho, D.L.A., Lake, D., Welsh, C.T., Castillo, M.: Apparent diffusion coefficients for differentiation of cerebellar tumors in children. AJNR Am. J. Neuroradiol. 27(6), 1362–1369 (2006)

    Google Scholar 

  15. Oka, K., et al.: Usefulness of diffusion-weighted imaging for differentiating between desmoid tumors and malignant soft tissue tumors. J. Magn. Reson. Imaging 33(1), 189–193 (2010)

    Article  Google Scholar 

  16. Abdulghaffar, W., Tag-Aldeen, M.: Role of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) in differentiating between benign and malignant breast lesions. Egypt. J. Radiol. Nucl. Med. 44(4), 945–951 (2013)

    Article  Google Scholar 

  17. Gupta, V.K., Liu, W., Wang, R., Ye, Y., Jiang, J.: Differentiation between benign and malignant breast lesions using ADC on diffusion-weighted imaging at 3.0 T. Open J. Radiol. 6(1), 1 (2016)

    Article  Google Scholar 

  18. Tsushima, Y., Taketomi, A., Endo, K.: Magnetic resonance (MR) differential diagnosis of breast tumors using apparent diffusion coefficient (ADC) on 1.5-T. J. Magn. Reson. Imaging 30(2), 249–255 (2009)

    Article  Google Scholar 

  19. Woodhams, R., et al.: ADC mapping of benign and malignant breast tumors. Magn. Reson. Med. Sci. 4(1), 35–42 (2005)

    Article  Google Scholar 

  20. Tsushima, Y., Takahashi-Taketomi, A., Endo, K.: Magnetic resonance (MR) differential diagnosis of breast tumors using apparent diffusion coefficient (ADC) on 1.5-T. J. Magn. Reson. Imaging 30(2), 249–255 (2009)

    Article  Google Scholar 

  21. Rosenkrantz, A.B., et al.: Whole-lesion diffusion metrics for assessment of bladder cancer aggressiveness. Abdom. Imaging 40(2), 327 (2015)

    Article  Google Scholar 

  22. Allam, K.E., Shalaby, M.H., Moulood, I.A.: Role of diffusion weighted MRI imaging in detection of liver metakstases. J. Magn. Reson. Imaging 69(5), 249–255 (2017)

    Google Scholar 

  23. Delgado, A.F., et al.: Diffusion kurtosis imaging of gliomas grades II and III – a study of perilesional tumor infiltration, tumor grades and subtypes at clinical presentation. Radiol. Oncol. 51(2), 121–129 (2017)

    Article  MathSciNet  Google Scholar 

  24. Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Automated fractured bone segmentation and labeling from CT images. J. Med. Syst. 43(3), 60 (2019)

    Article  Google Scholar 

  25. Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Segmentation and analysis of CT images for bone fracture detection and labeling, chap 7. In: Medical imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. CRC Press, Boca Raton (2019). ISBN 9780367139612

    Google Scholar 

  26. Ruikar, D.D., Hegadi, R.S., Santosh, K.C.: A systematic review on orthopedic simulators for psycho-motor skill and surgical procedure training. J. Med. Syst. 42(9), 168 (2018)

    Article  Google Scholar 

Download references

Acknowledgments

We would like to appreciate the supervision, encouragement and guidance provided by Dr. M. L. Jayatilake and Dr. B. S. Weerakoon to make this study success, also all the staff members at the Department of Radiography/Radiotherapy, University of Peradeniya and all the staff members at the department of radiology, Nawaloka Hospital Colombo, who helped us in many ways are gratefully acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sahan M. Vijithananda .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vijithananda, S.M. et al. (2019). Skewness and Kurtosis of Apparent Diffusion Coefficient in Human Brain Lesions to Distinguish Benign and Malignant Using MRI. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9184-2_17

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9183-5

  • Online ISBN: 978-981-13-9184-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics