Advertisement

Brain tumor detection based on extreme learning

  • 82 Accesses

Abstract

Gliomas are dreadful and common type of brain tumor. Therefore, treatment planning is significant to increase the survival rate of gliomas patients. The large structural and spatial variation between tumors makes an automated detection more challenging. Brain magnetic resonance imaging is utilized for tumor evaluation on the basis of automated segmentation and classification methods. In this work, triangular fuzzy median filtering is applied for image enhancement that helps in accurate segmentation based on unsupervised fuzzy set method. Gabor features are extracted across each candidate’s lesions, and similar texture (ST) features are calculated. These ST features are supplied to extreme learning machine (ELM), and regression ELM leaves one out for tumor classification. The technique is evaluated on BRATS 2012, 2013, 2014 and 2015 challenging datasets as well as on 2013 Leader board. The proposed approach shows better results and less computational time.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. 1.

    Zhao L, Jia K (2015) Deep feature learning with discrimination mechanism for brain tumor segmentation and diagnosis. In: 2015 International conference on intelligent information hiding and multimedia signal processing (IIH-MSP). IEEE, pp 306–309

  2. 2.

    Moise D, Madhusoodanan S (2006) Psychiatric symptoms associated with brain tumors: a clinical enigma. CNS Spectr 11(1):28–31

  3. 3.

    Morrison J (2015) When psychological problems mask medical disorders: a guide for psychotherapists. Guilford Publications, New York

  4. 4.

    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 1–20

  5. 5.

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

  6. 6.

    Amin J, Sharif M, Raza M, Yasmin M (2018) Detection of brain tumor based on features fusion and machine learning. J Ambient Intell Humaniz Comput 1–17

  7. 7.

    DeAngelis LM (2001) Brain tumors. New Engl J Med 344(2):114–123

  8. 8.

    Kleihues P, Burger PC, Scheithauer BW (1993) The new WHO classification of brain tumours. Brain Pathol 3(3):255–268

  9. 9.

    Naik J, Patel S (2014) Tumor detection and classification using decision tree in brain MRI. Int J Comput Sci Netw Sec (IJCSNS) 14(6):87

  10. 10.

    American Brain Tumor Association (ABTA). www.abta.org

  11. 11.

    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

  12. 12.

    Gordillo N, Montseny E, Sobrevilla P (2013) State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 31(8):1426–1438

  13. 13.

    Demirhan A, Törü M, Güler İ (2015) Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks. IEEE J Biomed Health Inform 19(4):1451–1458

  14. 14.

    Madhukumar S, Santhiyakumari N (2015) Evaluation of k-means and fuzzy C-means segmentation on MR images of brain. Egypt J Radiol Nucl Med 46(2):475–479

  15. 15.

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

  16. 16.

    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

  17. 17.

    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

  18. 18.

    Coatrieux G, Huang H, Shu H, Luo L, Roux C (2013) A watermarking-based medical image integrity control system and an image moment signature for tampering characterization. IEEE J Biomed Health Inform 17(6):1057–1067

  19. 19.

    Xie K, Yang J, Zhang Z, Zhu Y (2005) Semi-automated brain tumor and edema segmentation using MRI. Eur J Radiol 56(1):12–19

  20. 20.

    Naz I, Muhammad N, Yasmin M, Sharif M, Shah JH, Fernandes SL (2019) Robust discrimination of leukocytes protuberant types for early diagnosis of leukemia. J Mech Med Biol 19:1950055

  21. 21.

    Rajinikanth V, Satapathy SC, Dey N, Vijayarajan R (2018) DWT-PCA image fusion technique to improve segmentation accuracy in brain tumor analysis. In: Microelectronics, electromagnetics and telecommunications. Springer, pp 453–462

  22. 22.

    Amin J, Sharif M, Gul N, Yasmin M, Shad SA (2019) Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recognit Lett 129:115–122

  23. 23.

    Sharif M, Amin J, Raza M, Yasmin M, Satapathy SC (2019) An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor. Pattern Recognit Lett 129:129

  24. 24.

    Kaus MR, Warfield SK, Nabavi A, Chatzidakis E, Black PM, Jolesz FA, Kikinis R (1999) Segmentation of meningiomas and low grade gliomas in MRI. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, Heidelberg, pp 1–10

  25. 25.

    Moon N, Bullitt E, Van Leemput K, Gerig G (2002) Model-based brain and tumor segmentation. In: ICPR, pp 528–531

  26. 26.

    Prastawa M, Bullitt E, Ho S, Gerig G (2004) A brain tumor segmentation framework based on outlier detection. Med Image Anal 8(3):275–283

  27. 27.

    Corso JJ, Sharon E, Yuille A (2006) Multilevel segmentation and integrated Bayesian model classification with an application to brain tumor segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, Heidelberg, pp 790–798

  28. 28.

    Moonis G, Liu J, Udupa JK, Hackney DB (2002) Estimation of tumor volume with fuzzy-connectedness segmentation of MR images. Am J Neuroradiol 23(3):356–363

  29. 29.

    Dou W, Ruan S, Chen Y, Bloyet D, Constans J-M (2007) A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images. Image Vis Comput 25(2):164–171

  30. 30.

    Schmidt M, Levner I, Greiner R, Murtha A, Bistritz A (2005) Segmenting brain tumors using alignment-based features. In: Fourth international conference on machine learning and applications (ICMLA'05). IEEE, 6 p

  31. 31.

    Lefohn AE, Cates JE, Whitaker RT (2003) Interactive, GPU-based level sets for 3D segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, Heidelberg, pp 564–572

  32. 32.

    Ho S, Bullitt E, Gerig G (2002) Level-set evolution with region competition: automatic 3-D segmentation of brain tumors. In: Null. IEEE, p 10532

  33. 33.

    Zhu Y, Yan Z (1997) Computerized tumor boundary detection using a Hopfield neural network. IEEE Trans Med Imaging 16(1):55–67

  34. 34.

    Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

  35. 35.

    Pan Y, Huang W, Lin Z, Zhu W, Zhou J, Wong J, Ding Z (2015) Brain tumor grading based on neural networks and convolutional neural networks. In: 2015 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 699–702

  36. 36.

    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. Future Gener Comput Syst 88:28–39

  37. 37.

    Ansari GJ, Shah JH, Yasmin M, Sharif M, Fernandes SL (2018) A novel machine learning approach for scene text extraction. Future Gener Comput Syst 87:328–340

  38. 38.

    Ravı D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang G-Z (2017) Deep learning for health informatics. IEEE J Biomed Health Inform 21(1):4–21

  39. 39.

    Erhan D, Bengio Y, Courville A, Manzagol P-A, Vincent P, Bengio S (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11:625–660

  40. 40.

    Sharif M, Khan MA, Faisal M, Yasmin M, Fernandes SL (2018) A framework for offline signature verification system: best features selection approach. Pattern Recognit Lett 1–10

  41. 41.

    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. Curr Med Imaging Rev 14(1):77–87

  42. 42.

    Naqi S, Sharif M, Yasmin M, Fernandes SL (2018) Lung nodule detection using polygon approximation and hybrid features from CT images. Curr Med Imaging Rev 14(1):108–117

  43. 43.

    Liaqat A, Khan MA, Shah JH, Sharif M, Yasmin M, Fernandes SL (2018) Automated ulcer and bleeding classification from WCE images using multiple features fusion and selection. J Mech Med Biol 18:1850038

  44. 44.

    Ahmed KB, Hall LO, Goldgof DB, Liu R, Gatenby RA (2017) Fine-tuning convolutional deep features for MRI based brain tumor classification. In: Medical imaging 2017: computer-aided diagnosis, vol 10134. International Society for Optics and Photonics, p 101342E

  45. 45.

    Huang G-B (2015) What are extreme learning machines? Filling the gap between Frank Rosenblatt’s dream and John von Neumann’s puzzle. Cogn Comput 7(3):263–278

  46. 46.

    Haidong S, Hongkai J, Xingqiu L, Shuaipeng W (2018) Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowl Based Syst 140:1–14

  47. 47.

    Amin J, Sharif M, Yasmin M, Fernandes SL (2018) Big data analysis for brain tumor detection: deep convolutional neural networks. Future Gener Comput Syst 87:290–297

  48. 48.

    Biradar N, Dewal M, Rohit MK (2014) Edge preserved speckle noise reduction using integrated fuzzy filters. Int Sch Res Notices 2014:1–11

  49. 49.

    Bezdek JC (2013) Pattern recognition with fuzzy objective function algorithms. Springer, Berlin

  50. 50.

    Haghighat M, Zonouz S, Abdel-Mottaleb M (2015) CloudID: trustworthy cloud-based and cross-enterprise biometric identification. Expert Syst Appl 42(21):7905–7916

  51. 51.

    van Heeswijk M, Miche Y (2015) Binary/ternary extreme learning machines. Neurocomputing 149:187–197

  52. 52.

    Kistler et al (2013) The virtual skeleton database: an open access repository for biomedical research and collaboration. JMIR

  53. 53.

    Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y (2018) A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 43:98–111

  54. 54.

    Bakas S et al (2017) Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 4:170117

  55. 55.

    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 Assist Radiol Surg 9(2):241–253

  56. 56.

    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, pp 1–4

  57. 57.

    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

  58. 58.

    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

  59. 59.

    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

  60. 60.

    Goetz M, Weber C, Bloecher J, Stieltjes B, Meinzer H-P, Maier-Hein K (2014) Extremely randomized trees based brain tumor segmentation. In: Proceeding of BRATS challenge-MICCAI, p 006-011

  61. 61.

    Dvořák P, Menze B (2015) Local structure prediction with convolutional neural networks for multimodal brain tumor segmentation. In: International MICCAI workshop on medical computer vision. Springer, Cham, pp 59–71

  62. 62.

    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

  63. 63.

    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

  64. 64.

    Chen S, Ding C, Liu M (2019) Dual-force convolutional neural networks for accurate brain tumor segmentation. Pattern Recognit 88:90–100

  65. 65.

    Tustison NJ, Shrinidhi K, Wintermark M, Durst CR, Kandel BM, Gee JC, Grossman MC, Avants BB (2015) Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13(2):209–225

  66. 66.

    Kwon D, Akbari H, Da X, Gaonkar B, Davatzikos C (2014) Multimodal brain tumor image segmentation using GLISTR. In: MICCAI brain tumor segmentation (BraTS) challenge manuscripts. pp 18–19

  67. 67.

    Zikic D, Ioannou Y, Brown M, Criminisi A (2014) Segmentation of brain tumor tissues with convolutional neural networks. In: Proceedings MICCAI-BRATS, pp 36–39

  68. 68.

    Reza SMS, Mays R, Iftekharuddin KM (2015) Multi-fractal detrended texture feature for brain tumor classification. In: Medical imaging 2015: computer-aided diagnosis, vol 9414. International Society for Optics and Photonics, p 941410

  69. 69.

    Kwon D, Shinohara RT, Akbari H, Davatzikos C (2014) Combining generative models for multifocal glioma segmentation and registration. Med Image Comput Comput Assist Interv 17(Pt 1):763–770

Download references

Author information

Correspondence to Mudassar Raza.

Ethics declarations

Conflict of interest

This work entitled “Brain Tumor Detection based on Extreme Learning” is not submitted anywhere else. Whole content used in this research is original and not copied. There is no conflict of interest from authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sharif, M., Amin, J., Raza, M. et al. Brain tumor detection based on extreme learning. Neural Comput & Applic (2020) doi:10.1007/s00521-019-04679-8

Download citation

Keywords

  • Fuzzy rules
  • Erosion
  • MRI
  • Dilation
  • Gabor filter
  • Gliomas