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Brain MRI Modality Understanding: A Guide for Image Processing and Segmentation

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Bioinformatics and Biomedical Engineering (IWBBIO 2020)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12108))

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Abstract

Medical image processing is a highly challenging research area, thus medical imaging techniques are used to make diagnosis in human body. Moreover, as tumor in the brain is a critical and medical complaint, segmentation of the images has an important role to make segmentation of the brain tumor and it provides suspicious region diagnosis from the medical images. By the help of MRI scanners, signals generated by the human body tissues could be detected and determined spatially. Thus, we in this paper try to propose basics of MRI image modalities as a guide for understanding the processes and methods. Since original brain image is not appropriate for the examination, segmentation of the images could be very useful method for partition of the digital image into similar regions. This research also presents a guide for understanding the brain MRI sequences in other words modalities.

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References

  1. Kubicek, J., et al.: Design and analysis of LMMSE filter for MR image data. In: Nguyen, N.T., Gaol, F.L., Hong, T.-P., Trawiński, B. (eds.) ACIIDS 2019. LNCS (LNAI), vol. 11432, pp. 336–348. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14802-7_29

    Chapter  Google Scholar 

  2. Stankiewicz, J.M., et al.: Brain MRI lesion load at 1.5T and 3T versus clinical status in multiple sclerosis. J. Neuroimaging 21(2), e50–e56 (2011). https://doi.org/10.1111/j.1552-6569.2009.00449.x

    Article  PubMed  PubMed Central  Google Scholar 

  3. Balafar, M.A., Ramli, A.R., Saripan, M.I., Mashohor, S.: Review of brain MRI image segmentation methods. Artif. Intell. Rev. 33(3), 261–274 (2010). https://doi.org/10.1007/s10462-010-9155-0

    Article  Google Scholar 

  4. Alpar, O., Krejcar, O.: A comparative study on chrominance based methods in dorsal hand recognition: single image case. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds.) IEA/AIE 2018. LNCS (LNAI), vol. 10868, pp. 711–721. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92058-0_68

    Chapter  Google Scholar 

  5. Alpar, O., Krejcar, O.: Quantization and equalization of pseudocolor images in hand thermography. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2017. LNCS, vol. 10208, pp. 397–407. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56148-6_35

    Chapter  Google Scholar 

  6. Chang, P.-L., Teng, W.-G.: Exploiting the self-organizing map for medical image segmentation. Presented at the Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS 2007), Maribor, Slovenia (2007). https://doi.org/10.1109/CBMS.2007.48

  7. Marek, T., Krejcar, O., Selamat, A.: Possibilities for development and use of 3D applications on the android platform. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.-P. (eds.) ACIIDS 2016. LNCS (LNAI), vol. 9622, pp. 519–529. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49390-8_51

    Chapter  Google Scholar 

  8. Novotny, J., Dvorak, J., Krejcar, O.: User based intelligent adaptation of five in a row game for android based on the data from the front camera. In: De Paolis, L.T., Mongelli, A. (eds.) AVR 2016. LNCS, vol. 9768, pp. 133–149. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40621-3_9

    Chapter  Google Scholar 

  9. Hall, L.O., Bensaid, A.M., Clarke, L.P., Velthuizen, R.P., Silbiger, M.S., Bezdek, J.: A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans. Neural Netw. 3, 672–682 (1992)

    Article  CAS  Google Scholar 

  10. Kubicek, J., et al.: Autonomous segmentation and modeling of brain pathological findings based on iterative segmentation from MR images. In: Nguyen, N.T., Gaol, F.L., Hong, T.-P., Trawiński, B. (eds.) ACIIDS 2019. LNCS (LNAI), vol. 11432, pp. 324–335. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14802-7_28

    Chapter  Google Scholar 

  11. Alpar, O., Krejcar, O.: Thermal imaging for localization of anterior forearm subcutaneous veins. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2018. LNCS, vol. 10814, pp. 243–254. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78759-6_23

    Chapter  Google Scholar 

  12. Dolezal, R., et al.: Variable elimination approaches for data-noise reduction in 3D QSAR calculations. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds.) EPIA 2015. LNCS (LNAI), vol. 9273, pp. 313–325. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23485-4_33

    Chapter  Google Scholar 

  13. Filippi, M., et al.: Assessment of lesions on magnetic resonance imaging in multiple sclerosis: practical guidelines. Brain 142(7), 1858–1875 (2019). https://doi.org/10.1093/brain/awz144

    Article  PubMed  PubMed Central  Google Scholar 

  14. Samuel, T., Assefa, D., Krejcar, O.: Framework for effective image processing to enhance tuberculosis diagnosis. In: Nguyen, N.T., Hoang, D.H., Hong, T.-P., Pham, H., Trawiński, B. (eds.) ACIIDS 2018. LNCS (LNAI), vol. 10752, pp. 376–384. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75420-8_36

    Chapter  Google Scholar 

  15. Kunes, M., et al.: Imaging and evaluating method as part of endoscopical diagnostic approaches. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) ACIIDS 2014. LNCS (LNAI), vol. 8398, pp. 605–614. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05458-2_62

    Chapter  Google Scholar 

  16. Loizou, C.P., et al.: Brain image and lesions registration and 3D reconstruction in DICOM MRI images. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Thessaloniki, pp. 419–422 (2017). https://doi.org/10.1109/CBMS.2017.53

  17. Qiao, J., et al.: Data on MRI brain lesion segmentation using K-means and Gaussian mixture model-expectation maximization. Data Brief 27, 104628 (2019). https://doi.org/10.1016/j.dib.2019.104628

    Article  PubMed  PubMed Central  Google Scholar 

  18. Xue, Y., et al.: A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images. NeuroImage Clin. 25, 102118 (2020). https://doi.org/10.1016/j.nicl.2019.102118

    Article  PubMed  Google Scholar 

  19. Mohr, D.C., et al.: Psychological stress and the subsequent appearance of new brain MRI lesions in MS. Neurology 55(1), 55–61 (2000). https://doi.org/10.1212/WNL.55.1.55

    Article  CAS  PubMed  Google Scholar 

  20. Preston, D.C.: Magnetic resonance imaging (MRI) of the brain and spine: basics. Magnetic Resonance Imaging (MRI) of the Brain and Spine: Basics (2006). https://casemed.case.edu/clerkships/neurology/Web%20Neurorad/MRI%20Basics.htm. Accessed 04 Jan 2020

  21. Usman, K., Rajpoot, K.: Brain tumor classification from multi-modality MRI using wavelets and machine learning. Patt. Anal. Appl. 20(3), 871–881 (2017). https://doi.org/10.1007/s10044-017-0597-8

    Article  Google Scholar 

  22. Novozámský, A., Flusser, J., Tachecí, I., Sulík, L., Bureš, J., Krejcar, O.: Automatic blood detection in capsule endoscopy video. J. Biomed. Opt. 21(12) (2016). https://doi.org/10.1117/1.jbo.21.12.126007

  23. Chen, X., et al.: A prediction model of brain edema after endovascular treatment in patients with acute ischemic stroke. J. Neurol. Sci. 407, 116507 (2019). https://doi.org/10.1016/j.jns.2019.116507

    Article  PubMed  Google Scholar 

  24. Nakano, T., et al.: Goreisan prevents brain edema after cerebral ischemic stroke by inhibiting Aquaporin 4 upregulation in mice. J. Stroke Cerebrovasc. Dis. 27(3), 758–763 (2018). https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.10.010

    Article  PubMed  Google Scholar 

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Acknowledgement

This work is partially supported by the project of SPEV 2020, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-SPEV-2020) and project of the Ministry of Education, Youth and Sports of Czech Republic (project ERDF no. CZ.02.1.01/0.0/0.0/18_069/0010054).

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Authors and Affiliations

  1. Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Kralove, Czech Republic

    Ayca Kirimtat, Ondrej Krejcar & Ali Selamat

  2. Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia

    Ali Selamat

Authors
  1. Ayca Kirimtat
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  2. Ondrej Krejcar
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  3. Ali Selamat
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Corresponding author

Correspondence to Ondrej Krejcar .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Olga Valenzuela

  3. University of Granada, Granada, Spain

    Fernando Rojas

  4. University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Chicago and Fundacion Progreso y Salud, Granada, Spain

    Francisco Ortuño

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Kirimtat, A., Krejcar, O., Selamat, A. (2020). Brain MRI Modality Understanding: A Guide for Image Processing and Segmentation. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_63

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  • DOI: https://doi.org/10.1007/978-3-030-45385-5_63

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  • Print ISBN: 978-3-030-45384-8

  • Online ISBN: 978-3-030-45385-5

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