Skip to main content

A Computational Segmentation Tool for Processing Patient Brain MRI Image Data to Automatically Extract Gray and White Matter Regions

  • 525 Accesses

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 882)

Abstract

Brain MRI imaging is necessary to screen and detect diseases in the brain, and this requires processing, extracting, and analyzing a patient’s MRI medical image data. Neurologists and neurological clinicians, technicians, and researchers would be greatly facilitated and benefited by a graphical user interface-based computational tool that could perform all the required medical MRI image processing functions automatically, thus minimizing the cost, effort, and time required in screening disease from the patient’s MRI medical image data. Thus, there is a need for automatic medical image processing software platforms and for developing tools with applications in the medical field to assist neurologists, scientists, doctors, and academicians to analyze medical image data automatically to obtain patient-specific clinical parameters and information. This research develops an automatic brain MRI segmentation computational tool with a wide range of neurological applications to detect brain patients’ disease by analyzing the special clinical parameters extracted from the images and to provide patient-specific medical care, which can be especially helpful at early stages of the disease. The automatic brain MRI segmentation is performed based on modified pixel classification technique called fuzzy c-means followed by connected component labeling.

Keywords

  • Segmentation
  • Medical imaging
  • Fuzzy c-means
  • Neurological application

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-13-5953-8_1
  • Chapter length: 16 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   389.00
Price excludes VAT (USA)
  • ISBN: 978-981-13-5953-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   499.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Shasidhar, M., Raja, V. S. and Kumar, B. V., (June 2011). MRI brain image segmentation using modified fuzzy c-means clustering algorithm. In 2011 International Conference on Communication Systems and Network Technologies (CSNT) (pp. 473–478).

    Google Scholar 

  2. Despotović, I., Goossens, B., Philips, W. (2015) MRI segmentation of the human brain: challenges, methods, and applications. Computational and mathematical methods in medicine, 2015.

    CrossRef  Google Scholar 

  3. Gordillo, N., Montseny, E., & Sobrevilla, P. (2013). State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8), 1426–1438.

    CrossRef  Google Scholar 

  4. Suhag, S., & Saini, L. M. (May 2015). Automatic detection of brain tumor by image processing in matlab. In SARC-IRF International Conference.

    Google Scholar 

  5. Hassan, E., & Aboshgifa, A. (2015). Detecting brain tumour from MRI image using matlab gui programme. International Journal of Computer Science & Engineering Survey (IJCSES) 6(6).

    Google Scholar 

  6. Sharma, N., & Aggarwal, L. M. (2010). Automated medical image segmentation techniques. Journal of medical physics/Association of Medical Physicists of India, 35(1), 3.

    Google Scholar 

  7. Tsai, C., Manjunath, B. S., & Jagadeesan, B. (1995). Automated segmentation of brain MR images. Pattern Recognition, 28(12), 1825–1837.

    CrossRef  Google Scholar 

  8. Despotović, I., Goossens, B., & Philips, W. (2015). MRI segmentation of the human brain: challenges, methods, and applications. Computational and mathematical methods in medicine2015.

    Google Scholar 

  9. Chuang, K. S., Tzeng, H. L., Chen, S., Wu, J., & Chen, T. J. (2006). Fuzzy c-means clustering with spatial information for image segmentation. Computerized medical imaging and graphics30(1), pp. 9–15.

    Google Scholar 

  10. Mokbel, H. A., Morsy, M. E. S., & Abou-Chadi, F. E. Z. (2000). Automatic segmentation and labeling of human brain tissue from MR images. In 17th NRSC’2000. Seventeenth National Radio Science Conference, 2000 (pp. K2–1). IEEE.

    Google Scholar 

  11. Antolovic, D., (2008). Review of the Hough transform method, with an implementation of the fast Hough variant for line detection. Department of Computer Science, Indiana University.

    Google Scholar 

  12. Kumar, N., & Nachamai, M. Noise Removal and filtering techniques used in medical images. Oriental Journal of Computer Science and Tachnology 10(1).

    Google Scholar 

  13. Wang, H. R., Yang, J. L., Sun, H. J., Chen, D., & Liu, X. L. (August 2011). An improved region growing method for medical image selection and evaluation based on Canny edge detection. In 2011 International Conference on Management and Service Science (MASS) (pp. 1–4). IEEE.

    Google Scholar 

  14. Mubarak, D. M. N., Sathik, M. M., Beevi, S. Z., & Revathy, K. (2012). A hybrid region growing algorithm for medical image segmentation. International Journal of Computer Science & Information Technology, 4(3), 61.

    CrossRef  Google Scholar 

  15. Wong, K. K., Tu, J., Kelso, R. M., Worthley, S. G., Sanders, P., Mazumdar, J., et al. (2010). Cardiac flow component analysis. Medical Engineering & Physics, 32(2), 174–188.

    CrossRef  Google Scholar 

  16. Zanaty, E. A. (2013). An Approach based on fusion concepts for improving brain magnetic resonance images (MRIs) segmentation. Journal of Medical Imaging and Health Informatics, 3(1), 30–37.

    CrossRef  Google Scholar 

  17. Zanaty, E. A., & Ghiduk, A. S. (2013). A novel approach for medical image segmentation based on genetic and seed region growing algorithms. Journal of Computer Science and Information Systems ComSIS, 10(3), 1319–1342.

    CrossRef  Google Scholar 

  18. Zanaty, E. A., & Afifi, A. (2013). A watershed approach for improving medical image segmentation. Computer methods in biomechanics and biomedical engineering, 16(12), 1262–1272.

    CrossRef  Google Scholar 

  19. Zanaty, E. A. (2013). An adaptive fuzzy C-means algorithm for improving MRI segmentation. Open Journal of Medical Imaging, 3(04), 125.

    CrossRef  Google Scholar 

  20. [Online] Available: https://en.wikipedia.org/wiki/Connected-component_labeling [Accessed November 9, 2017].

  21. Wu, K., Otoo, E., & Shoshani, A. (2005). Optimizing connected component labeling algorithms. Lawrence Berkeley National Laboratory.

    Google Scholar 

  22. Suzuki, K., Horiba, I., & Sugie, N. (2003). Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding, 89(1), 1–23.

    CrossRef  Google Scholar 

  23. Goyal, A., Lee, J., Lamata, P., van den Wijngaard, J., van Horssen, P., Spaan, J., et al. (2013). Model-based vasculature extraction from optical fluorescence cryomicrotome images. IEEE Transactions on Medical Imaging, 32(1), 56–72.

    CrossRef  Google Scholar 

  24. Sikarwar, B. S., Roy, M. K., Ranjan, P., & Goyal, A. (2016). Automatic Disease Screening Method Using Image Processing for Dried Blood Microfluidic Drop Stain Pattern Recognition. Journal of Medical Engineering & Technology, 40(5), 245–254.

    CrossRef  Google Scholar 

  25. Sikarwar, B. S., Roy, M. K., Ranjan, P., & Goyal, A. (2016). Imaging-based method for precursors of impending disease from blood traces. In Advances in Intelligent Systems and Computing (Vol. 468, pp. 411–424). Springer.

    Google Scholar 

  26. Sikarwar, B. S., Roy, M. K., Ranjan, P., &  Goyal, A. (2015). Automatic pattern recognition for detection of disease from blood drop stain obtained with microfluidic device. In Advances in Intelligent Systems and Computing (Vol. 425, pp. 655–667). Springer.

    Google Scholar 

  27. Bhan, A., Bathla, D., & Goyal, A. (2016). Patient-specific cardiac computational modeling based on left ventricle segmentation from magnetic resonance images. Advances in Intelligent Systems and Computing (Vol. 469, pp. 179–187). Springer.

    Google Scholar 

  28. Ray, V., & Goyal, A. (2015) Automatic left ventricle segmentation in cardiac MRI images using a membership clustering and heuristic region-based pixel classification approach. In  Advances in Intelligent Systems and Computing (Vol. 425, pp. 615–623). Springer.

    Google Scholar 

  29. Chhabra, M., & Goyal, A. (2017) Accurate and robust iris recognition using modified classical hough transform. In Lecture Notes in Networks and Systems (Vol. 10, pp. 493–507). Springer.

    Google Scholar 

  30. Goyal, A., & Ray, V. (2015). Belongingness clustering and region labeling based pixel classification for automatic left ventricle segmentation in cardiac MRI images. Translational Biomedicine, 6(3).

    Google Scholar 

  31. Goyal, A., Roy, M., Gupta, P., Dutta, M. K., Singh, S., & Garg, V. (2015) Automatic detection of mycobacterium tuberculosis in stained sputum and urine smear images. Archives of Clinical Microbiology, 6(3).

    Google Scholar 

  32. Bhan, A., Goyal, A., Chauhan, N., & Wang, C.W. (2016) Feature line profile based automatic detection of dental caries in bitewing radiography. In: International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE), pp. 635–640, IEEE.

    Google Scholar 

  33. Bhan, A., Goyal, A., Dutta, M. K., Riha, K., Omran, Y. Image-Based Pixel Clustering and Connected Component Labeling in Left Ventricle Segmentation of Cardiac MR Images. In 7th International Congress on Ultra-Modern Telecommunications and Control Systems and Workshops (ICUMT), pp. 339–342, IEEE, 2015.

    Google Scholar 

  34. Ray, V., & Goyal, A. (2015). Image-Based fuzzy c-means clustering and connected component labeling subsecond fast fully automatic complete cardiac cycle left ventricle segmentation in multi frame cardiac MRI images. In International Conference on Systems in Medicine and Biology (ICSMB), IEEE.

    Google Scholar 

  35. Goyal, A., van den Wijngaard, J., van Horssen, P., Grau, V., Spaan, J., & Smith, N. (2009). Intramural spatial variation of optical tissue properties measured with fluorescence microsphere images of porcine cardiac tissue. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1408–1411.

    Google Scholar 

  36. Sharma, P., Sharma, S., Goyal, A. (2016) An MSE (mean square error) based analysis of deconvolution techniques used for deblurring/restoration of MRI and CT Images. In 2nd International Conference on Information and Communication Technology for Competitive Strategies (ICTCS-2016), March 04–05, 2016, Udaipur, India, Conference Proceedings by ACM—ICPS Proceedings Vol. ISBN 978-1-4503-3962-9/16/03, http://dx.doi.org/10.1145/2905055.2905257.

  37. Goyal, A., Bathla, D., Sharma, P., Sahay, M., & Sood, S. (2016). MRI image based patient specific computational model reconstruction of the left ventricle cavity and myocardium. In 2016 International Conference on Computing, Communication and Automation (ICCCA), pp. 1065–1068, IEEE.

    Google Scholar 

  38. Duta, M., Thiyagalingam, J., Trefethen, A., Goyal, A., Grau, V., & Smith, N. (2010) Parallel simulation for parameter estimation of optical tissue properties. In Euro-Par 2010-Parallel Processing (pp. 51–62).

    Google Scholar 

  39. Atkins, M. S., & Mackiewich, B. T. (1998). Fully automatic segmentation of the brain in MRI. IEEE Transactions on Medical Imaging, 17(1), 98–107.

    CrossRef  Google Scholar 

  40. Wagner, M., Yang, P., Schafer, S., Strother, C., & Mistretta, C. (2015). Noise reduction for curve-linear structures in real time fluoroscopy applications using directional binary masks. Medical Physics, 42(8), 4645–4653.

    CrossRef  Google Scholar 

  41. Meijs, M., Patel, A., Leemput, S. C., Prokop, M., Dijk, E. J., Leeuw, F. E., et al. (2017). Robust segmentation of the full cerebral vasculature in 4D CT of suspected stroke patients. Scientific reports, 7(1), 15622.

    CrossRef  Google Scholar 

  42. Bhan, A., Goyal, A., & Ray, V. (2015) Fast fully automatic multiframe segmentation of left ventricle in cardiac mri images using local adaptive k-means clustering and connected component labeling. In 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 114–119, IEEE.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ayush Goyal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Goyal, A. et al. (2019). A Computational Segmentation Tool for Processing Patient Brain MRI Image Data to Automatically Extract Gray and White Matter Regions. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 882. Springer, Singapore. https://doi.org/10.1007/978-981-13-5953-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-5953-8_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-5952-1

  • Online ISBN: 978-981-13-5953-8

  • eBook Packages: EngineeringEngineering (R0)