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Hybrid algorithms for brain tumor segmentation, classification and feature extraction


The brain tumor is a cancerous disease due to the growth of abnormal cells in the human brain. It causes the death of many precious lives because of inaccurate calculation and identification of brain tumor. The average annual mortality rate of brain tumors in the United States between 2010 to 2014 was 4.33%, and almost 10,190 men and 7,830 women died this year from a brain tumor and the average survival rate in 5 years brain tumor is 36%. Much research has been done in the biomedical image processing field using computing concepts to segment and classifies brain tumors accurately. However, the diverse image content, occlusion, noisy image, chaotic object, nonuniform image texture, and other factors badly affect the performance of image clustering and segmentation algorithms. Therefore it is required to model an automatic image segmentation and classification algorithm. This research aims to segment brain tumors from MRI images using threshold segmentation and watershed algorithm and then classify brain tumors on features extracted (MSER, FAST, Harlick, etc.) through different classifiers. The proposed methodology includes image acquisition, image pre-processing, image segmentation, and feature extraction. Different classifiers are used to classify brain tumors from the datasets used accurately. The results indicate that the proposed mechanism enhances the detection of brain tumor images than the existing techniques by achieving more than 90% accuracy.

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Maximally stable extremal regions


Features from accelerated segment test


Gray level co matrix


Support vector machine


MATRIX laborarty


K nearest neighbor


Magnetic resonance imaging


Machine learning


Histogram of oriented gradient


  1. Abdelmohsen K, Gorospe M (2010) Posttranscriptional regulation of cancer traits by HuR. Wires RNA 1(2):214–229

    Article  Google Scholar 

  2. Ahmadian S, Norouzi-Fard A, Svensson O, Ward J (2019) Better guarantees for k-means and euclidean k-median by primal-dual algorithms. SIAM J Comput 49:FOCS17-97-FOCS17-156

    MathSciNet  Article  Google Scholar 

  3. Alam MS, Rahman MM, Hossain MA, Islam MK, Ahmed KM, Ahmed KT, Singh BC, Miah MS (2019) Automatic human brain tumor detection in MRI image using template-based k means and improved fuzzy C means clustering algorithm. Big Data Cogn Comput 3(2):27

    Article  Google Scholar 

  4. Azhari EM, Hatta M, Htike ZZ, Win SL (2014) Rain tumor detection and localization in magnetic resonance imaging. IJITCS 4(1):1939–2231

    Google Scholar 

  5. Bauer S, Nolte LP, Reyes M (2011) Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 354–361

    Google Scholar 

  6. Borole VY, Nimbhore SS, Kawthekar DS (2015) Image processing techniques for brain tumor detection: a review. IJETTCS 4(5):2

    Google Scholar 

  7. Chen YJ (2015) Deblending using a space-varying median filter. Explor Geophys 46(4):332–341

    Article  Google Scholar 

  8. Deepak S, Ameer PM (2021) Automated categorization of brain tumor from mri using cnn features and svm. J Ambient Intell Humaniz Comput 12(8):8357–8369

    Article  Google Scholar 

  9. Dilber D, Jasleen (2016) Brain tumor detection using watershed Algorithm. Int J Innov Res Sci Eng Technol.

    Article  Google Scholar 

  10. Dong H, Yang G, Liu F, Mo Y, Guo Y (2017) Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. annual conference on medical image understanding and analysis. Springer, Berlin, pp 506–517

    Google Scholar 

  11. Fan J, Zhao F (2007) Two-dimensional Otsu’s curve thresholding segmentation method for gray-level images. Acta Electron Sin 35(4):751

    Google Scholar 

  12. Guennouni S, Ahaitouf A, Mansouri A (2017) Face detection: comparing haar-like combined with cascade classifiers and edge orientation matching. In: International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), IEEE, pp 1–4

  13. Gupta T, Manocha P, Gandhi TK, Gupta R, Panigrahi B (2017) Tumor classification and segmentation of MR brain images. arXiv preprint arXiv:1710.11309

  14. Hebli AP, Gupta S (2016) Brain tumor detection using image processing: a survey. In: Proceedings of 65th IRF International Conference, 20th

  15. Hrosik RC, Tuba E, Dolicanin E, Jovanovic R, Tuba M (2019) Brain image segmentation based on firefly algorithm combined with k-means clustering. Stud Inform Control 28:167–176

    Google Scholar 

  16. Huang H, Meng F, Zhou S, Jiang F, Manogaran G (2019) Brain image segmentation based on FCM clustering algorithm and rough set. IEEE Access 7:12386–12396

    Article  Google Scholar 

  17. Islam MA, Yousuf MS, Billah M (2019) Automatic plant detection using HOG and LBP features with SVM. Int J Comput 33(1):26–38

    Google Scholar 

  18. Kose N, Apvrille L, Dugelay JL (2015) Facial makeup detection technique based on texture and shape analysis. In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), IEEE, pp 1–7

  19. Lin GC, Wang CM, Wang WJ, Sun SY (2010) Automated classification of multispectral MR images using unsupervised constrained energy minimization based on fuzzy logic. Magn Reson Imaging 28(5):721–738

    Article  Google Scholar 

  20. Liu Yh, Muftah M, Das T, Bai L, Robson K, Auer D (2012) Classificatioo of MR tumor images based on gabor wavelet analysis. J Med Biol Eng 32(1):22–28

    Article  Google Scholar 

  21. 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):578–595

    MathSciNet  Article  Google Scholar 

  22. Liu L, Lao S, Fieguth PW, Guo Y, Wang X, Pietikäinen M (2016) Median robust extended local binary pattern for texture classification. IEEE Trans Image Process 25(3):1368–1381

    MathSciNet  Article  Google Scholar 

  23. Liu C, Liu W, Xing W (2017) An improved edge-based level set method combining local regional fitting information for noisy image segmentation. Signal Process 130:12–21

    Article  Google Scholar 

  24. Matsuda H, Mizumura S, Soma T, Takemura N (2004) Conversion of brain SPECT images between different collimators and reconstruction processes for analysis using statistical parametric mapping. Nucl Med Commun 25(1):67–74

    Article  Google Scholar 

  25. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R (2014) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993–2024

    Article  Google Scholar 

  26. Mittal M, Goyal LM, Kaur S, Kaur I, Verma A, Hemanth D (2019) Deep learning based enhanced tumor segmentation approach for MR brain images. Appl Soft Comput 78:346–354

    Article  Google Scholar 

  27. Padmavathy T, Vimalkumar M, Nagarajan S, Babu GC, Parthasarathy P (2018) Performance analysis of pre-cancerous mammographic image enhancement feature using non-subsampled shearlet transform. Multimed Tools Appl.

    Article  Google Scholar 

  28. Patil RC, Bhalchandra A (2012) Brain tumour extraction from MRI images using MATLAB. IJECSCSE 2(1):1

    Google Scholar 

  29. Prasanna D, Prabhakar M (2019) An effiecient human tracking system using Haar-like and hog feature extraction. Clust Comput 22(2):2993–3000

    Article  Google Scholar 

  30. Rajinikanth V, Fernandes SL, Bhushan B, Sunder N (2018) Segmentation and analysis of brain tumor using Tsallis entropy and regularised level set. Proceedings of 2nd international conference on micro-electronics, electromagnetics and telecommunications. Springer, Berlin, pp 313–321

    Chapter  Google Scholar 

  31. Roy S, Maji P (2015) A simple skull stripping algorithm for brain MRI. In: Eighth International Conference on Advances in Pattern Recognition (ICAPR), IEEE, pp 1–6

  32. Sasaki Y (1970) Numerical variational analysis formulated under the constraints as determined by longwave equations and a low-pass filter. Monthly Weather Review 98(12):884–898

    Article  Google Scholar 

  33. Senthilkumar C, Gnanamurthy R (2019) A fuzzy clustering based MRI brain image segmentation using back propagation neural networks. Clust Comput 22(5):12305–12312

    Article  Google Scholar 

  34. Sharif M, Tanvir U, Munir EU, Khan MA, Yasmin M (2018) ’Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection. J Ambient Intell Humaniz Comput.

    Article  Google Scholar 

  35. Visa S, Ramsay B, Ralescu AL, Van Der Knaap E (2011) Confusion matrix-based feature selection. MAICS 710:120–127

    Google Scholar 

  36. Wahid F, Ghazali R, Fayaz M, Shah A (2016) Using probabilistic classification technique and statistical features for brain magnetic resonance imaging (MRI) Classification: an application of AI technique in bio-science. Int J Bio-Sci Bio-Technol 8(6):93–106

    Article  Google Scholar 

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Correspondence to Rashid Amin.

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Habib, H., Amin, R., Ahmed, B. et al. Hybrid algorithms for brain tumor segmentation, classification and feature extraction. J Ambient Intell Human Comput (2021).

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  • MRI
  • Watershed
  • Threshold
  • Brain tumors
  • MSER
  • Gabor wavelet
  • HOG
  • Tree
  • Ensemble
  • Logistic regression