Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection

  • Muhammad Sharif
  • Uroosha Tanvir
  • Ehsan Ullah Munir
  • Muhammad Attique KhanEmail author
  • Mussarat YasminEmail author
Original Research


A malignant tumor in brain is detected using images from Magnetic Resonance scanners. Malignancy detection in brain and separation of its tissues from normal brain cells allows to correctly localizing abnormal tissues in brain’s Magnetic Resonance Imaging (MRI). In this article, a new method is proposed for the segmentation and classification of brain tumor based on improved saliency segmentation and best features selection approach. The presented method works in four pipe line procedures such as tumor preprocessing, tumor segmentation, feature extraction and classification. In the first step, preprocessing is performed to extract the region of interest (ROI) using manual skull stripping and noise effects are removed by Gaussian filter. Then tumor is segmented in the second step by improved thresholding method which is implemented by binomial mean, variance and standard deviation. In the third step, geometric and four texture features are extracted. The extracted features are fused by a serial based method and best features are selected using Genetic Algorithm (GA). Finally, support vector machine (SVM) of linear kernel function is utilized for the classification of selected features. The proposed method is tested on two data sets including Harvard and Private. The Private data set is collected from Nishtar Hospital Multan, Pakistan. The proposed method achieved average classification accuracy of above 90% for both data sets which shows its authenticity.


Brain MRI Thresholding Geometric features Texture features SVM 



  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. Acunzo DJ, MacKenzie G, van Rossum MC (2012) Systematic biases in early ERP and ERF components as a result of high-pass filtering. Journal of neuroscience methods 209(1):212–218CrossRefGoogle Scholar
  3. Ajaj K, Syed NA (2015) Image processing techniques for automatic detection of tumor in human brain using SVM. Int J Adv Res Comput Commun Eng 4(4):147–171Google Scholar
  4. Albregtsen F (2008) Statistical texture measures computed from gray level coocurrence matrices. Image Processing Laboratory, Department of Informatics, University of Oslo, version 5Google Scholar
  5. Amin J, Sharif M, Yasmin M, Fernandes SL (2018) Big data analysis for brain tumor detection: deep convolutional neural networks. Future Gener Comput Syst 87:290–297CrossRefGoogle Scholar
  6. Ariyo O, Zhi-guang Q, Tian L (2017) Brain MR segmentation using a fusion of K-means and spatial fuzzy C-means. In: 2017 International conference on computer science and application engineering (CSAE 2017), pp 863–873Google Scholar
  7. Ashour AS, Beagum S, Dey N, Ashour AS, Pistolla DS, Nguyen GN, Shi F (2018a) Light microscopy image de-noising using optimized LPA-ICI filter. Neural Comput Appl 29(12):1517–1533CrossRefGoogle Scholar
  8. Ashour DS, Rayia DMA, Dey N, Ashour AS, Hawas AR, Alotaibi MB (2018b) Schistosomal hepatic fibrosis classification. IJNCR 7(2):1–17Google Scholar
  9. Avina-Cervantes JG, Lindner D, Guerrero-Turrubiates J, Chalopin C (2016) Automatic brain tumor tissue detection based on hierarchical centroid shape descriptor in Tl-weighted MR images. In: Paper presented at the 2016 international conference on electronics, communications and computers (CONIELECOMP)Google Scholar
  10. Bahadure NB, Ray AK, Thethi HP (2018) Comparative approach of MRI-based brain tumor segmentation and classification using genetic algorithm. J Digit Imaging, 1–13Google Scholar
  11. Bauer S, Nolte L-P, Reyes M (2011) Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Paper presented at the international conference on medical image computing and computer-assisted interventionGoogle Scholar
  12. Benson CC, Lajish VL, Rajamani K (2016) A novel skull stripping and enhancement algorithm for the improved brain tumor segmentation using mathematical morphology. Int J Image Graph Signal Process 8:59–66Google Scholar
  13. Calabrese C, Poppleton H, Kocak M, Hogg TL, Fuller C, Hamner B,.. . Allen M (2007) A perivascular niche for brain tumor stem cells. Cancer cell 11(1):69–82CrossRefGoogle Scholar
  14. Chaddad A (2015) Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models. Int J Biomed Imaging 2015:868031. CrossRefGoogle Scholar
  15. Chaddad A, Zinn PO, Colen RR (2014) Brain tumor identification using Gaussian Mixture model features and decision trees classifier. In: Paper presented at the information sciences and systems (CISS), 2014 48th annual conference onGoogle Scholar
  16. Chua AS, Egorova S, Anderson MC, Polgar-Turcsanyi M, Chitnis T, Weiner HL, Healy BC (2015) Using multiple imputation to efficiently correct cerebral MRI whole brain lesion and atrophy data in patients with multiple sclerosis. NeuroImage 119:81–88CrossRefGoogle Scholar
  17. Damodharan S, Raghavan D (2015) Combining tissue segmentation and neural network for brain tumor detection. IAJIT. 12:1Google Scholar
  18. Deng G, Cahill L (1993) An adaptive Gaussian filter for noise reduction and edge detection. In: Paper presented at the nuclear science symposium and medical imaging conference, 1993, 1993 IEEE conference recordGoogle Scholar
  19. Desai U, Martis RJ, Nayak G, Seshikala C, Sarika G, Shetty K (2016) Decision support system for arrhythmia beats using ECG signals with DCT, DWT and EMD methods: a comparative study. J Mech Med Biol 16(01):1640012CrossRefGoogle Scholar
  20. Doshi J, Erus G, Ou Y, Gaonkar B, Davatzikos C (2013) Multi-atlas skull-stripping. Acad Radiol 20(12):1566–1576CrossRefGoogle Scholar
  21. El Abbadi NK, Kadhim NE (2017) Brain cancer classification based on features and artificial neural network. Brain 6:1Google Scholar
  22. Gao Y, Mas JF, Kerle N, Navarrete Pacheco JA (2011) Optimal region growing segmentation and its effect on classification accuracy. Int J Remote Sens 32(13):3747–3763CrossRefGoogle Scholar
  23. Hamamci A, Kucuk N, Karaman K, Engin K, Unal G (2012) Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans Med Imaging 31(3):790–804CrossRefGoogle Scholar
  24. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31CrossRefGoogle Scholar
  25. Hore S, Chakroborty S, Ashour AS, Dey N, Ashour AS, Sifaki-Pistolla D, Chaudhuri S (2015) Finding contours of hippocampus brain cell using microscopic image analysis. J Adv Microsc Res 10(2):93–103CrossRefGoogle Scholar
  26. Irum I, Shahid M, Sharif M, Raza M (2015) A review of image denoising methods. J Eng Sci Technol Rev. 8:5Google Scholar
  27. Kadam DB, Gade SS et al (2012) Neural network based brain tumor detection using MR images. Int J Comput Sci Commun 2(2):325–331Google Scholar
  28. Kalaivani A, Chitrakala S (2018) An optimal multi-level backward feature subset selection for object recognition. IETE J Res 2:1–13CrossRefGoogle Scholar
  29. Kalavathi P, Prasath VS (2016) Methods on skull stripping of MRI head scan images—a review. J Digit Imaging 29(3):365–379CrossRefGoogle Scholar
  30. Kaya IE, Pehlivanlı A, Sekizkardeş EG, Ibrikci T (2017) PCA based clustering for brain tumor segmentation of T1w MRI images. Comput Methods Programs Biomed 140:19–28CrossRefGoogle Scholar
  31. 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. CrossRefGoogle Scholar
  32. Khan MA, Akram T, Sharif M, Javed MY, Muhammad N, Yasmin M (2018) An implementation of optimized framework for action classification using multilayers neural network on selected fused features. Pat Anal Appl 6:1–21Google Scholar
  33. Kumar R, Mathai KJ (2017) Brain Tumor segmentation by modified K-mean with morphological operations. Brain 6:8Google Scholar
  34. Lenz M, Krug R, Dillmann C, Stroop R, Gerhardt NC, Welp H, Hofmann MR (2018) Automated differentiation between meningioma and healthy brain tissue based on optical coherence tomography ex vivo images using texture features. J Biomed Opt 23(7):071205CrossRefGoogle Scholar
  35. Liu J, Li M, Wang J, Wu F, Liu T, Pan Y (2014) A survey of MRI-based brain tumor segmentation methods. Tsinghua Sci Technol 19(6):578–595MathSciNetCrossRefGoogle Scholar
  36. Masood S, Sharif M, Yasmin M, Raza M, Mohsin S (2013) Brain image compression: a brief survey. Res J Appl Sci 5(1):49–59Google Scholar
  37. Menon N, Ramakrishnan R (2015) Brain tumor segmentation in MRI images using unsupervised artificial bee colony algorithm and FCM clustering. In: Paper presented at the Communications and Signal Processing (ICCSP), 2015 International Conference onGoogle Scholar
  38. Moraru L, Moldovanu S, Dimitrievici LT, Ashour AS, Dey N (2016) Texture anisotropy technique in brain degenerative diseases. Neural Computing and Appl 5:1–11Google Scholar
  39. Moraru L, Moldovanu S, Dimitrievici LT, Shi F, Ashour AS, Dey N (2017) Quantitative diffusion tensor magnetic resonance imaging signal characteristics in the human brain: a hemispheres analysis. IEEE Sens J 17(15):4886–4893CrossRefGoogle Scholar
  40. Mosquera JM, Sboner A, Zhang L, Chen CL, Sung YS, Chen HW, Singer S (2013) Novel MIR143-NOTCH fusions in benign and malignant glomus tumors. Genes Chromosom Cancer 52(11):1075–1087CrossRefGoogle Scholar
  41. Nabizadeh N, Kubat M (2015) Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Comput Electr Eng 45:286–301CrossRefGoogle Scholar
  42. 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 TechGoogle Scholar
  43. Nazir M, Wahid F, Ali Khan S (2015) A simple and intelligent approach for brain MRI classification. J Intell Fuzzy Syst 28(3):1127–1135MathSciNetGoogle Scholar
  44. Ohgaki H, Kleihues P (2013) The definition of primary and secondary glioblastoma. Clin Cancer Res 19(4):764–772CrossRefGoogle Scholar
  45. Patil S, Udupi V (2012) Preprocessing to be considered for MR and CT images containing tumors. IOSR J Electrical Electron Eng 1(4):54–57CrossRefGoogle Scholar
  46. 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
  47. Popuri K, Cobzas D, Murtha A, Jägersand M (2012) 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set. Int J Comput Assis Radiol Surg 7(4):493–506CrossRefGoogle Scholar
  48. Rajinikanth V, Satapathy SC, Dey N, Vijayarajan R (2018) DWT-PCA image fusion technique to improve segmentation accuracy in brain tumor analysis microelectronics. In: Anguera J, Satapathy S, Bhateja V, Sunitha K (eds) Microelectronics, electromagnetics and telecommunications. Lecture notes in electrical engineering, vol 471. Springer, SingaporeGoogle Scholar
  49. Rani J, Kumar R, Talukdar FA, Dey N (2017) The brain tumor segmentation using fuzzy c-means technique: a study. In: Recent advances in applied thermal imaging for industrial applications. IGI Global, pp 40–61Google Scholar
  50. Raza M, Sharif M, Yasmin M, Masood S, Mohsin S (2012) Brain image representation and rendering: a survey. Res J Appl Sci 21:4Google Scholar
  51. Samanta S, Ahmed SS, Salem MA-MM, Nath SS, Dey N, Chowdhury SS (2015) Haralick features based automated glaucoma classification using back propagation neural network. In: Paper presented at the proceedings of the 3rd international conference on frontiers of intelligent computing: theory and applications (FICTA) 2014Google Scholar
  52. Senthilkumaran N, Thimmiaraja J (2014) Histogram equalization for image enhancement using MRI brain images. Paper presented at the Computing and Communication Technologies (WCCCT), 2014 World Congress onGoogle Scholar
  53. Sharif M, Khan MA, Akram T, Javed MY, Saba T, Rehman A (2017) A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features selection. EURASIP J Image Video Proces 2017(1):89CrossRefGoogle Scholar
  54. Sharif M, Khan MA, Faisal M, Yasmin M, Fernandes SL (2018) A framework for offline signature verification system: Best features selection approach. Pattern Recogn LettGoogle Scholar
  55. Sharma M, Purohit G, Mukherjee S (2018) Information retrieves from brain MRI images for tumor detection using hybrid technique K-means and artificial neural network (KMANN) networking communication and data knowledge engineering. Springer, Heidelberg pp. 145–157Google Scholar
  56. Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Ye X (2018) Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Comput Method Program BiomedGoogle Scholar
  57. Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Proces Lett 9(3):293–300CrossRefGoogle Scholar
  58. Tian Z, Dey N, Ashour AS, McCauley P, Shi F (2017) Morphological segmenting and neighborhood pixel-based locality preserving projection on brain fMRI dataset for semantic feature extraction: an affective computing study. Neural Comput Appl 5:1–16Google Scholar
  59. Vishnuvarthanan G, Rajasekaran MP, Subbaraj P, Vishnuvarthanan A (2016) An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl Soft Comput 38:190–212CrossRefGoogle Scholar
  60. Wang S, Du S, Atangana A, Liu A, Lu Z (2016) Application of stationary wavelet entropy in pathological brain detection. Multimedia Tools Appl 2:1–14Google Scholar
  61. Wang Y, Shi F, Cao L, Dey N, Wu Q, Ashour AS,.. . Wu L (2018) Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images. Curr BioinformGoogle Scholar
  62. Wu W, Chen AY, Zhao L, Corso JJ (2014) Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features. Int J Comput Assis Radiol Surg 9(2):241–253CrossRefGoogle Scholar
  63. Yang G, Zhang Y, Yang J, Ji G, Dong Z, Wang S, Wang Q (2015) Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimed Tools Appl 1:1–17Google Scholar
  64. Yasmin M, Sharif M, Mohsin S, Azam F (2014) Pathological brain image segmentation and classification: a survey. Curr Med Imaging Rev 10(3):163–177CrossRefGoogle Scholar
  65. Zhang YD, Chen S, Wang SH, Yang JF, Phillips P (2015) Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. Int J Imaging Syst Technol 25(4):317–327CrossRefGoogle Scholar
  66. Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom K,.. . Napel S (2018) Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. Am J Neuroradiol 39(2):208–216CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceCOMSATS University IslamabadIslamabadPakistan
  2. 2.Department of Computer Science and EngineeringHITEC UniversityTaxilaPakistan

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