Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 10, pp 12723–12748 | Cite as

An efficient computerized decision support system for the analysis and 3D visualization of brain tumor

  • Irfan Mehmood
  • Muhammad SajjadEmail author
  • Khan Muhammad
  • Syed Inayat Ali Shah
  • Arun Kumar Sangaiah
  • Muhammad Shoaib
  • Sung Wook Baik
Article

Abstract

The quality of health services provided by medical centers varies widely, and there is often a large gap between the optimal standard of services when judged based on the locality of patients (rural or urban environments). This quality gap can have serious health consequences and major implications for patient’s timely and correct treatment. These deficiencies can manifest, for example, as a lack of quality services, misdiagnosis, medication errors, and unavailability of trained professionals. In medical imaging, MRI analysis assists radiologists and surgeons in developing patient treatment plans. Accurate segmentation of anomalous tissues and its correct 3D visualization plays an important role inappropriate treatment. In this context, we aim to develop an intelligent computer-aided diagnostic system focusing on human brain MRI analysis. We present brain tumor detection, segmentation, and its 3D visualization system, providing quality clinical services, regardless of geographical location, and level of expertise of medical specialists. In this research, brain magnetic resonance (MR) images are segmented using a semi-automatic and adaptive threshold selection method. After segmentation, the tumor is classified into malignant and benign based on a bag of words (BoW) driven robust support vector machine (SVM) classification model. The BoW feature extraction method is further amplified via speeded up robust features (SURF) incorporating its procedure of interest point selection. Finally, 3D visualization of the brain and tumor is achieved using volume marching cube algorithm which is used for rendering medical data. The effectiveness of the proposed system is verified over a dataset collected from 30 patients and achieved 99% accuracy. A subjective comparative analysis is also carried out between the proposed method and two state-of-the-art tools ITK-SNAP and 3D-Doctor. Experimental results indicate that the proposed system performed better than existing systems and assists radiologist determining the size, shape, and location of the tumor in the human brain.

Keywords

Medical image processing Tumor segmentation and classification MRI images Medical imaging MRI 3D visualization 

Notes

Acknowledgments

This research was supported by the Korean MSIT (Ministry of Science and ICT), under the National Program for Excellence in SW (2015-0-00938), supervised by the IITP (Institute for Information & communications Technology Promotion).

References

  1. 1.
    Abdellah M, Eldeib A, Sharawi A (2015) High performance GPU-based Fourier volume rendering. Journal of Biomedical Imaging 2015:2Google Scholar
  2. 2.
    Ahmad A, Dey L (2007) A k-mean clustering algorithm for mixed numeric and categorical data. Data Knowl Eng 63(2):503–527CrossRefGoogle Scholar
  3. 3.
    Algohary AO et al (2010) Improved segmentation technique to detect cardiac infarction in MRI C-SENC images. In: Biomedical Engineering Conference (CIBEC), 2010 5th Cairo International. IEEEGoogle Scholar
  4. 4.
    Ateeq T et al (2018) Ensemble-classifiers-assisted detection of cerebral microbleeds in brain MRI. Comput Electr EngGoogle Scholar
  5. 5.
    Bagheri MA, Montazer GA, Escalera S (2012) Error correcting output codes for multiclass classification: application to two image vision problems. In: Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on. IEEEGoogle Scholar
  6. 6.
    Bozorgi M, Lindseth F (2015) GPU-based multi-volume ray casting within VTK for medical applications. Int J Comput Assist Radiol Surg 10(3):293–300CrossRefGoogle Scholar
  7. 7.
    Chen Y-T (2012) Brain tumor detection using three-dimensional Bayesian level set method with volume rendering. In: Wavelet Analysis and Pattern Recognition (ICWAPR), 2012 International Conference on. IEEEGoogle Scholar
  8. 8.
    Dai Y et al (2013) Volume-rendering-based interactive 3D measurement for quantitative analysis of 3D medical images. Comput Math Methods Med 2013Google Scholar
  9. 9.
    Das AJ, Mahanta LB, Prasad V (2014) Automatic detection of brain tumor from MR Images using morphological operations and K-means based segmentationGoogle Scholar
  10. 10.
    Deng W et al (2010) MRI brain tumor segmentation with region growing method based on the gradients and variances along and inside of the boundary curve. In: Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on. IEEEGoogle Scholar
  11. 11.
    Despotović I, Goossens B, Philips W (2015) MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med 2015Google Scholar
  12. 12.
    El-Dahshan E-SA, Hosny T, Salem A-BM (2010) Hybrid intelligent techniques for MRI brain images classification. Digital Signal Process 20(2):433–441CrossRefGoogle Scholar
  13. 13.
    Ghosh S, Dubey SK (2013) Comparative analysis of k-means and fuzzy c-means algorithms. Int J Adv Comput Sci Appl:4(4)Google Scholar
  14. 14.
    Gong F, Zhao X (2010) Three-dimensional reconstruction of medical image based on improved marching cubes algorithm. In: Machine Vision and Human-Machine Interface (MVHI), 2010 International Conference on. IEEEGoogle Scholar
  15. 15.
    Har-Peled S, Roth D, Zimak D (2003) Constraint classification for multiclass classification and ranking. In: Advances in neural information processing systemsGoogle Scholar
  16. 16.
    Hohne KH (2002) Medical image computing at the institute of mathematics and computer science in medicine, university hospital hamburg-eppendorf. IEEE Trans Med Imaging 21(7):713–723CrossRefGoogle Scholar
  17. 17.
    Jaffar MA et al (2012) Anisotropic diffusion based brain MRI segmentation and 3D reconstruction. International Journal of Computational Intelligence Systems 5(3):494–504CrossRefGoogle Scholar
  18. 18.
    Juan-Albarracín J et al (2015) Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification. PLoS One 10(5):e0125143CrossRefGoogle Scholar
  19. 19.
    Khotanlou H et al (2009) 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets Syst 160(10):1457–1473MathSciNetCrossRefGoogle Scholar
  20. 20.
    Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3D surface construction algorithm. In: ACM siggraph computer graphics. ACMGoogle Scholar
  21. 21.
    Louis DN et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131(6):803–820CrossRefGoogle Scholar
  22. 22.
    Mehmood I et al (2013) Prioritization of brain MRI volumes using medical image perception model and tumor region segmentation. Comput Biol Med 43(10):1471–1483CrossRefGoogle Scholar
  23. 23.
    Mehmood I, Sajjad M, Baik SW (2014) Video summarization based tele-endoscopy: a service to efficiently manage visual data generated during wireless capsule endoscopy procedure. J Med Syst 38(9):109CrossRefGoogle Scholar
  24. 24.
    Mehmood I, Sajjad M, Baik SW (2014) Mobile-cloud assisted video summarization framework for efficient management of remote sensing data generated by wireless capsule sensors. Sensors 14(9):17112–17145CrossRefGoogle Scholar
  25. 25.
    Natarajan P et al (2012) Tumor detection using threshold operation in MRI brain images. In: Computational Intelligence & Computing Research (ICCIC), 2012 I.E. International Conference on. IEEEGoogle Scholar
  26. 26.
    Rajesh Sharma R, Marikkannu P (2015) Hybrid RGSA and support vector machine framework for three-dimensional magnetic resonance brain tumor classification. Sci World J:2015Google Scholar
  27. 27.
    Ray D, Majumder DD, Das A (2012) Noise reduction and image enhancement of MRI using adaptive multiscale data condensation. In: Recent Advances in Information Technology (RAIT), 2012 1st International Conference on. IEEEGoogle Scholar
  28. 28.
    Vrji KA, Jayakumari J (2011) Automatic detection of brain tumor based on magnetic resonance image using CAD System with watershed segmentation. In: Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), 2011 International Conference on. IEEEGoogle Scholar
  29. 29.
    Wang T, Cheng I, Basu A (2010) Fully automatic brain tumor segmentation using a normalized Gaussian Bayesian classifier and 3D fluid vector flow. In: Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEEGoogle Scholar
  30. 30.
    Yang G et al (2016) Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimedia Tools and Applications 75(23):15601–15617CrossRefGoogle Scholar
  31. 31.
    Yazdani S et al (2014) Magnetic resonance image tissue classification using an automatic method. Diagn Pathol 9(1):207CrossRefGoogle Scholar
  32. 32.
    Zakeri FS, Behnam H, Ahmadinejad N (2012) Classification of benign and malignant breast masses based on shape and texture features in sonography images. J Med Syst 36(3):1621–1627CrossRefGoogle Scholar
  33. 33.
    Zhang H et al (2011) An automated and simple method for brain MR image extraction. Biomed Eng Online 10(1):81CrossRefGoogle Scholar
  34. 34.
    Zhang Y-D, Yuan T-F, Dong Z-C (2017) Brain imaging and automatic analysis in neurological and psychiatric diseases–part I. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 16(1):3–4Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of SoftwareSejong UniversitySeoulRepublic of Korea
  2. 2.Digital Image Processing Laboratory, Department of Computer ScienceIslamia College PeshawarPeshawarPakistan
  3. 3.Intelligent Media Laboratory, Digital Contents Research InstituteSejong UniversitySeoulRepublic of Korea
  4. 4.Department of MathematicsIslamia College PeshawarPeshawarPakistan
  5. 5.School of Computing Science and EngineeringVIT UniversityVelloreIndia

Personalised recommendations