Advertisement

Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions

  • Javeria Amin
  • Muhammad Sharif
  • Mussarat YasminEmail author
  • Tanzila Saba
  • Mudassar Raza
Article

Abstract

The physical appearance of a brain tumor in human beings may be an indication of problems in psychological (cognitive) functions. Such functions include learning, understanding, problem solving, decision making, and planning. Early brain tumor detection can be done by using the proper procedure of screening. MRI is used for the detection of disease staging and follow-up without ionization radiation. In this manuscript, an automated system is proposed for the analysis of brain data and detection of cognitive functions abnormalities. The region of interest (ROI) is enhanced using a proposed partial differential diffusion filter (PDDF) which is a modified form of anisotropic diffusion filter. Otsu algorithm is used for better segmentation. Moreover, a new method is also proposed for feature extraction which is a concatenation of local binary pattern (LBP) and Gray level co-occurrence matrix (C2LBPGLCM). The proposed method accurately distinguishes between healthy and unhealthy images with high specificity, sensitivity, and area under the curve.

Keywords

Magnetic resonance imaging (MRI) Gray level co-occurrence matrix (GLCM) Potential differential diffusion filter (PDDF) Local binary pattern (LBP) LBP based GLCM (C2LBPGLCM) 

Notes

Acknowledgements

This work is supported by Department of Computer Science, COMSATS University Islamabad, Wah Campus Pakistan. We are thankful to COMSATS for providing a strong research platform, fully equipped labs and other research facilities to make this work possible.

References

  1. 1.
    Abbasi S, Tajeripour F (2017) Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 219:526–535CrossRefGoogle Scholar
  2. 2.
    Abdulbaqi HS, Jafri MZM, Mutter KN, Omar AF, Mustafa IS, Abood LK (2016) Segmentation and estimation of brain tumor volume in magnetic resonance images based on T2-weighted using hidden Markov random field algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 8(3):9–13Google Scholar
  3. 3.
    Ain Q, Jaffar MA, Choi T-S (2014) Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor. Appl Soft Comput 21:330–340CrossRefGoogle Scholar
  4. 4.
    Alfonse M, Salem A-BM (2016) An automatic classification of brain tumors through MRI using support vector machine. Egyptian Computer Science Journal 40(3):11–21Google Scholar
  5. 5.
    Amin J, Sharif M, Yasmin M, Fernandes SL (2017) A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn Lett 1-10Google Scholar
  6. 6.
    Amin J, Sharif M, Yasmin M, Ali H, Fernandes SL (2017) A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions. Journal of Computational Science 19:153–164CrossRefGoogle Scholar
  7. 7.
    Amin J, Sharif M, Yasmin M, Fernandes SL (2018) Big data analysis for brain tumor detection: deep convolutional neural networks. Futur Gener Comput Syst 87:290–297Google Scholar
  8. 8.
    Ansari GJ, Shah JH, Yasmin M, Sharif M, Fernandes SL (2018) A novel machine learning approach for scene text extraction. Futur Gener Comput Syst 87:328-340Google Scholar
  9. 9.
    Bahadure NB, Ray AK, Thethi HP (2017) Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. International Journal of Biomedical Imaging 2017:1–12Google Scholar
  10. 10.
    Banerjee S, Mitra S, Shankar BU (2016) Single seed delineation of brain tumor using multi-thresholding. Inf Sci 330:88–103CrossRefGoogle Scholar
  11. 11.
    Banerjee S, Mitra S, Shankar BU (2018) Automated 3D segmentation of brain tumor using visual saliency. Inf Sci 424:337–353MathSciNetCrossRefGoogle Scholar
  12. 12.
    Bauer S, Fejes T, Slotboom J, Wiest R, Nolte L-P, Reyes M (2012) Segmentation of brain tumor images based on integrated hierarchical classification and regularization. In MICCAI BraTS Workshop. Miccai Society, NiceGoogle Scholar
  13. 13.
    Benson CC, Lajish VL (2014) Morphology based enhancement and skull stripping of MRI brain images. In: 2014 International Conference on Intelligent Computing Applications (ICICA) (pp. 254-257). IEEE, CoimbatoreGoogle Scholar
  14. 14.
    Binczyk F, Stjelties B, Weber C, Goetz M, Meier-Hein K, Meinzer H-P, Bobek-Billewicz B, Tarnawski R, Polanska J (2017) MiMSeg-an algorithm for automated detection of tumor tissue on NMR apparent diffusion coefficient maps. Inf Sci 384:235–248CrossRefGoogle Scholar
  15. 15.
    Bokhari F, Syedia T, Sharif M, Yasmin M, Fernandes SL (2018) Fundus image segmentation and feature extraction for the detection of glaucoma: a new approach. Current Medical Imaging Reviews 14(1):77–87CrossRefGoogle Scholar
  16. 16.
    Chen X, Konukoglu E (2018) Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. arXiv preprint arXiv:1806.04972Google Scholar
  17. 17.
    Damodharan S, Raghavan D (2015) Combining tissue segmentation and neural network for brain tumor detection. International Arab Journal of Information Technology (IAJIT) 12(1):42-52Google Scholar
  18. 18.
    Demirhan A, Törü M, Güler I (2015) Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks. IEEE Journal of Biomedical and Health Informatics 19(4):1451–1458CrossRefGoogle Scholar
  19. 19.
    Desai U, Martis RJ, Acharya UR, Nayak CG, Seshikala G, SHETTY K R (2016) Diagnosis of multiclass tachycardia beats using recurrence quantification analysis and ensemble classifiers. Journal of Mechanics in Medicine and Biology 16(01):1640005CrossRefGoogle Scholar
  20. 20.
    Desai U, Nayak CG, Seshikala G, Martis RJ, Fernandes SL (2018) Automated diagnosis of tachycardia beats. In Smart Computing and Informatics 77:421-429Google Scholar
  21. 21.
    Dong H, Yang G, Liu F, Mo Y, Guo Y (2017) Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. arXiv preprint arXiv:1705.03820Google Scholar
  22. 22.
    Dong H, Yang G, Liu F, Mo Y, Guo Y (2007) Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In Annual Conference on Medical Image Understanding and Analysis. Springer, Cham, pp 506-517Google Scholar
  23. 23.
    Fernandes SL, Chakraborty B, Gurupur VP, Prabhu G (2016) Early skin cancer detection using computer aided diagnosis techniques. J Integr Des Process Sci 20(1):33–43CrossRefGoogle Scholar
  24. 24.
    Gatenby RA, Grove O, Gillies RJ (2013) Quantitative imaging in cancer evolution and ecology. Radiology 269(1):8–14CrossRefGoogle Scholar
  25. 25.
    Goetz M, Weber C, Bloecher J, Stieltjes B, Meinzer H-P, Maier-Hein K (2014) Extremely randomized trees based brain tumor segmentation. Proceeding of BRATS Challenge-MICCAI:006–011Google Scholar
  26. 26.
    Gondal AH, Khan MNA (2013) A review of fully automated techniques for brain tumor detection from MR images. International Journal of Modern Education and Computer Science 5(2):55CrossRefGoogle Scholar
  27. 27.
    Guo X, Schwartz L, Zhao B (2013) Semi-automatic segmentation of multimodal brain tumor using active contours. Multimodal Brain Tumor Segmentation, 27Google Scholar
  28. 28.
    Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P-M, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31CrossRefGoogle Scholar
  29. 29.
    Holland EC (2000) Glioblastoma multiforme: the terminator. Proc Natl Acad Sci 97(12):6242–6244CrossRefGoogle Scholar
  30. 30.
    Huang M, Yang W, Wu Y, Jiang J, Chen W, Feng Q (2014) Brain tumor segmentation based on local independent projection-based classification. IEEE Trans Biomed Eng 61(10):2633–2645CrossRefGoogle Scholar
  31. 31.
    Huang Q, Yang F, Liu L, Li X (2015) Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis. Inf Sci 314:293–310CrossRefGoogle Scholar
  32. 32.
    Kadkhodaei M, Samavi S, Karimi N, Mohaghegh H, Soroushmehr SMR, Ward K, All A, Najarian K (2016) Automatic segmentation of multimodal brain tumor images based on classification of super-voxels. In Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the (pp. 5945-5948). IEEEGoogle Scholar
  33. 33.
    Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61–78CrossRefGoogle Scholar
  34. 34.
    Khan MW, Sharif M, Yasmin M, Fernandes SL (2016) A new approach of cup to disk ratio based glaucoma detection using fundus images. J Integr Des Process Sci 20(1):77–94CrossRefGoogle Scholar
  35. 35.
    Khan MA, Sharif M, Javed MY, Akram T, Yasmin M, Saba T (2017) License number plate recognition system using entropy-based features selection approach with SVM. IET Image Process 12(2):200–209CrossRefGoogle Scholar
  36. 36.
    Kong Y, Deng Y, Dai Q (2015) Discriminative clustering and feature selection for brain MRI segmentation. IEEE Signal Processing Letters 22(5):573–577CrossRefGoogle Scholar
  37. 37.
    Liaqat A, Khan MA, Shah JH, Sharif M, Yasmin M, Fernandes SL (2018) Automated ulver and bleeding classification from WCE images using multiple features fusion and selection. Journal of Mechanics in Medicine and Biology 18(4):1850038-1-1850038-25Google Scholar
  38. 38.
    Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114(2):97–109CrossRefGoogle Scholar
  39. 39.
    Meier R, Bauer S, Slotboom J, Wiest R, Reyes M (2013) A hybrid model for multimodal brain tumor segmentation. Multimodal Brain Tumor Segmentation 31:31–37Google Scholar
  40. 40.
    Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993–2024CrossRefGoogle Scholar
  41. 41.
    Mitra S, Shankar BU (2015) Medical image analysis for cancer management in natural computing framework. Inf Sci 306:111–131CrossRefGoogle Scholar
  42. 42.
    Naqi S, Sharif M, Yasmin M, Fernandes SL (2018) Lung nodule detection using polygon approximation and hybrid features from CT images. Current Medical Imaging Reviews 14(1):108–117CrossRefGoogle Scholar
  43. 43.
    Nasir M, Attique Khan M, Sharif M, Lali IU, Saba T, Iqbal T (2018) An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach. Microsc Res Tech 81(6):1-16Google Scholar
  44. 44.
    Nida N, Sharif M, Khan MUG, Yasmin M, Fernandes SL (2016) A framework for automatic colorization of medical imaging. IIOAB J 7:202–209Google Scholar
  45. 45.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987zbMATHCrossRefGoogle Scholar
  46. 46.
    Parmar C, Velazquez ER, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, Mitra S, Shankar BU, Kikinis R, Haibe-Kains B (2014) Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 9(7):e102107CrossRefGoogle Scholar
  47. 47.
    Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240–1251CrossRefGoogle Scholar
  48. 48.
    Rajinikanth V, Fernandes SL, Bhushan B, Sunder NR (2016) Segmentation and analysis of brain tumor using Tsallis entropy and regularised level set. In Proceedings of 2nd International Conference on Micro-Electronics, Electromagnetics and Telecommunications. Springer, Singapore, pp 313-321Google Scholar
  49. 49.
    Raza M, Sharif M, Yasmin M, Khan MA, Saba T, Fernandes SL (2018) Appearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learning. Futur Gener Comput Syst 88:28–39CrossRefGoogle Scholar
  50. 50.
    Reza SM, Mays R, Iftekharuddin KM (2015) Multi-fractal detrended texture feature for brain tumor classification. In Medical Imaging 2015: Computer-Aided Diagnosis (Vol. 9414, p. 941410). International Society for Optics and PhotonicsGoogle Scholar
  51. 51.
    Shah JH, Sharif M, Yasmin M, Fernandes SL (2017) Facial expressions classification and false label reduction using LDA and threefold SVM. Pattern Recogn Lett 1-8Google Scholar
  52. 52.
    Shil SK, Polly FP, Hossain MA, Ifthekhar MS, Uddin MN, Jang YM (2017) An improved brain tumor detection and classification mechanism. In 2017 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 54-57). IEEEGoogle Scholar
  53. 53.
    American Cancer Society (2012) Cancer facts & figures for Hispanic. American Cancer Society, AtlantaGoogle Scholar
  54. 54.
    Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X (2017) Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int J Comput Assist Radiol Surg 12(2):183–203CrossRefGoogle Scholar
  55. 55.
    Tajeripour F, Kabir E, Sheikhi A (2008) Fabric defect detection using modified local binary patterns. EURASIP Journal on Advances in Signal Processing 2008:60zbMATHGoogle Scholar
  56. 56.
    Torheim T, Malinen E, Kvaal K, Lyng H, Indahl UG, Andersen EK, Futsæther CM (2014) Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines. IEEE Trans Med Imaging 33(8):1648–1656CrossRefGoogle Scholar
  57. 57.
    Vezhnevets V, Konouchine V (2005) GrowCut: interactive multi-label ND image segmentation by cellular automata. In Proc. of Graphicon (Vol. 1, No. 4, pp. 150-156)Google Scholar
  58. 58.
    Yu J, Tao D, Wang M (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21(7):3262–3272MathSciNetzbMATHCrossRefGoogle Scholar
  59. 59.
    Yu J, Rui Y, Chen B (2014) Exploiting click constraints and multi-view features for image re-ranking. IEEE Transactions on Multimedia 16(1):159–168CrossRefGoogle Scholar
  60. 60.
    Zanaty E (2012) Determination of gray matter (GM) and white matter (WM) volume in brain magnetic resonance images (MRI). Int J Comput Appl 45(3):16–22Google Scholar
  61. 61.
    Zhao L, Sarikaya D, Corso JJ (2013) Automatic brain tumor segmentation with MRF on supervoxels. Multimodal Brain Tumor Segmentation 51Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Javeria Amin
    • 1
  • Muhammad Sharif
    • 1
  • Mussarat Yasmin
    • 1
    Email author
  • Tanzila Saba
    • 2
  • Mudassar Raza
    • 1
  1. 1.Department of Computer ScienceUniversity of WahWah CantonmentPakistan
  2. 2.College of Computer and Information SciencesPrince Sultan University Riyadh Saudi ArabiaRiyadhSaudi Arabia

Personalised recommendations