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

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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.

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References

  1. Abbasi S, Tajeripour F (2017) Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 219:526–535

    Article  Google Scholar 

  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):9–13

    Google Scholar 

  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 21:330–340

    Article  Google Scholar 

  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):11–21

  5. Amin J, Sharif M, Yasmin M, Fernandes SL (2017) A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn Lett 1-10

  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 19:153–164

    Article  Google Scholar 

  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 87:290–297

  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 87:328-340

  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 2017:1–12

  10. Banerjee S, Mitra S, Shankar BU (2016) Single seed delineation of brain tumor using multi-thresholding. Inf Sci 330:88–103

    Article  Google Scholar 

  11. Banerjee S, Mitra S, Shankar BU (2018) Automated 3D segmentation of brain tumor using visual saliency. Inf Sci 424:337–353

    Article  MathSciNet  Google Scholar 

  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, Nice

  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, Coimbatore

  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 384:235–248

    Article  Google Scholar 

  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):77–87

    Article  Google Scholar 

  16. Chen X, Konukoglu E (2018) Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. arXiv preprint arXiv:1806.04972

  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):42-52

  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):1451–1458

    Article  Google Scholar 

  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):1640005

    Article  Google Scholar 

  20. Desai U, Nayak CG, Seshikala G, Martis RJ, Fernandes SL (2018) Automated diagnosis of tachycardia beats. In Smart Computing and Informatics 77:421-429

  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:1705.03820

  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 506-517

  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):33–43

    Article  Google Scholar 

  24. Gatenby RA, Grove O, Gillies RJ (2013) Quantitative imaging in cancer evolution and ecology. Radiology 269(1):8–14

    Article  Google Scholar 

  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:006–011

  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):55

    Article  Google Scholar 

  27. Guo X, Schwartz L, Zhao B (2013) Semi-automatic segmentation of multimodal brain tumor using active contours. Multimodal Brain Tumor Segmentation, 27

  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 35:18–31

    Article  Google Scholar 

  29. Holland EC (2000) Glioblastoma multiforme: the terminator. Proc Natl Acad Sci 97(12):6242–6244

    Article  Google Scholar 

  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):2633–2645

    Article  Google Scholar 

  31. Huang Q, Yang F, Liu L, Li X (2015) Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis. Inf Sci 314:293–310

    Article  Google Scholar 

  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). IEEE

  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 36:61–78

    Article  Google Scholar 

  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):77–94

    Article  Google Scholar 

  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):200–209

    Article  Google Scholar 

  36. Kong Y, Deng Y, Dai Q (2015) Discriminative clustering and feature selection for brain MRI segmentation. IEEE Signal Processing Letters 22(5):573–577

    Article  Google Scholar 

  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):1850038-1-1850038-25

  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):97–109

    Article  Google Scholar 

  39. Meier R, Bauer S, Slotboom J, Wiest R, Reyes M (2013) A hybrid model for multimodal brain tumor segmentation. Multimodal Brain Tumor Segmentation 31:31–37

    Google Scholar 

  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):1993–2024

    Article  Google Scholar 

  41. Mitra S, Shankar BU (2015) Medical image analysis for cancer management in natural computing framework. Inf Sci 306:111–131

    Article  Google Scholar 

  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):108–117

    Article  Google Scholar 

  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):1-16

  44. Nida N, Sharif M, Khan MUG, Yasmin M, Fernandes SL (2016) A framework for automatic colorization of medical imaging. IIOAB J 7:202–209

  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):971–987

    Article  Google Scholar 

  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):e102107

    Article  Google Scholar 

  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):1240–1251

    Article  Google Scholar 

  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 313-321

  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 88:28–39

    Article  Google Scholar 

  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 Photonics

  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 1-8

  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). IEEE

  53. American Cancer Society (2012) Cancer facts & figures for Hispanic. American Cancer Society, Atlanta

  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):183–203

    Article  Google Scholar 

  55. Tajeripour F, Kabir E, Sheikhi A (2008) Fabric defect detection using modified local binary patterns. EURASIP Journal on Advances in Signal Processing 2008:60

    MATH  Google Scholar 

  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):1648–1656

    Article  Google Scholar 

  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)

  58. Yu J, Tao D, Wang M (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21(7):3262–3272

    Article  MathSciNet  Google Scholar 

  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):159–168

    Article  Google Scholar 

  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):16–22

    Google Scholar 

  61. Zhao L, Sarikaya D, Corso JJ (2013) Automatic brain tumor segmentation with MRF on supervoxels. Multimodal Brain Tumor Segmentation 51

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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.

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Authors and Affiliations

  1. Department of Computer Science, University of Wah, Wah Cantonment, Pakistan

    Javeria Amin, Muhammad Sharif, Mussarat Yasmin & Mudassar Raza

  2. College of Computer and Information Sciences, Prince Sultan University Riyadh Saudi Arabia, Riyadh, Saudi Arabia

    Tanzila Saba

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  1. Javeria Amin
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  2. Muhammad Sharif
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  3. Mussarat Yasmin
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  5. Mudassar Raza
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Correspondence to Mussarat Yasmin.

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Amin, J., Sharif, M., Yasmin, M. et al. Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions. Multimed Tools Appl 79, 10955–10973 (2020). https://doi.org/10.1007/s11042-019-7324-y

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