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A hybrid of active contour model and convex hull for automated brain tumor segmentation in multimodal MRI

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Abstract

Segmenting tumor automatically in human brain Magnetic Resonance (MR) images is challenging because of uneven, irregular and unstructured size and shape of the tumor. This paper proposes an automated two stage brain tumor segmentation method. In the first stage, coarse estimation of the brain tumor is carried out using convex hull approach. The coarse estimate thus obtained is employed as the initialization for the active contour model applied in the second stage thereby eliminating the need of human intervention. Multiscale Harris energy is estimated at different levels to identify high-energy regions and thereafter constructing convex hull over the selected key-points in order to detect the abnormality in the input MR images. The proposed method is applied to 2-d axial images of fluid attenuated inversion recovery (FLAIR) and post-contrast T1-weighted (T1c) MRI images from the brain tumor segmentation benchmark challenge 2015 (BRATS2015) dataset. Different sub-compartments of the tumor such as enhanced tumor, edema, and necrosis are segmented and in addition combined in the form of tumor core, complete tumor, and enhanced tumor labels. The proposed method is evaluated in terms of Dice Similarity Coefficient (DSC), Sensitivity and Positive Predictive Value (PPV). Average DSC score of 81% for brain tumor core, 92% for complete brain tumor, and 83% for enhanced brain tumor are achieved which is better than several state-of-the-art methods.

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Notes

  1. https://www.smir.ch/BRATS/Start2015

References

  1. Agn M, Puonti O, Law I, af Rosenschöld P, van Leemput K (2015) Brain tumor segmentation by a generative model with a prior on tumor shape. In: Proceeding of the multimodal brain tumor image segmentation challenge, pp 1–4

  2. Avola D, Cinque L (2008) Encephalic nmr image analysis by textural interpretation. In: Proceedings of the 2008 ACM symposium on applied computing. ACM, pp 1338–1342

  3. Avola D, Cinque L, Di Girolamo M (2011) A novel t-cad framework to support medical image analysis and reconstruction. In: International conference on image analysis and processing. Springer, pp 414–423

  4. Avola D, Cinque L, Placidi G (2013) Customized first and second order statistics based operators to support advanced texture analysis of mri images. Computational and mathematical methods in medicine 2013

  5. Bahadure NB, Ray AK, Thethi HP (2018) Comparative approach of mri-based brain tumor segmentation and classification using genetic algorithm. J Digit Imaging 31(4):477–489

    Article  Google Scholar 

  6. Banday SA, Mir AH (2017) Statistical textural feature and deformable model based brain tumor segmentation and volume estimation. Multimed Tools Appl 76 (3):3809–3828

    Article  Google Scholar 

  7. 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. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 354–361

  8. Bauer S, Wiest R, Nolte LP, Reyes M (2013) A survey of mri-based medical image analysis for brain tumor studies. Phys Med Biol 58(13):R97

    Article  Google Scholar 

  9. Bharath H, Colleman S, Sima D, Van Huffel S (2017) Tumor segmentation from multimodal mri using random forest with superpixel and tensor based feature extraction. In: International MICCAI brainlesion workshop. Springer, pp 463–473

  10. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277. https://doi.org/10.1109/83.902291

    Article  Google Scholar 

  11. Corso JJ, Sharon E, Dube S, El-Saden S, Sinha U, Yuille A (2008) Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Trans Med Imaging 27(5):629–640

    Article  Google Scholar 

  12. Cui S, Mao L, Jiang J, Liu C, Xiong S (2018) Automatic semantic segmentation of brain gliomas from mri images using a deep cascaded neural network, vol 2018

  13. Dice LR (1945) Measures of the amount of ecologic association between species. Ecol 26(3):297–302

    Article  Google Scholar 

  14. Drevelegas A, Nasel C (2010) Imaging of brain tumors with histological correlations. Springer Science & Business Media, New York

    Google Scholar 

  15. Dupont C, Betrouni N, Reyns N, Vermandel M (2016) On image segmentation methods applied to glioblastoma: state of art and new trends. IRBM 37(3):131–143

    Article  Google Scholar 

  16. Fletcher-Heath LM, Hall LO, Goldgof DB, Murtagh FR (2001) Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med 21(1-3):43–63

    Article  Google Scholar 

  17. Gong YJ, Zhou Y (2018) Differential evolutionary superpixel segmentation. IEEE Trans Image Process 27(3):1390–1404

    Article  MathSciNet  Google Scholar 

  18. Gonzalez RC, Woods RE (2002) Digital image processing, Third Edition. Publishing house of electronics industry

  19. Gupta N, Bhatele P, Khanna P (2019) Glioma detection on brain mris using texture and morphological features with ensemble learning. Biomed Signal Process Control 47:115–125

    Article  Google Scholar 

  20. 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):790–804

    Article  Google Scholar 

  21. Harris C, Stephens M (1988) A combined corner and edge detector. In: Alvey vision conference, vol 15. Citeseer, pp 147–151

  22. Hasan AM, Meziane F, Aspin R, Jalab HA (2016) Segmentation of brain tumors in mri images using three-dimensional active contour without edge. Symmetry 8(11):132

    Article  MathSciNet  Google Scholar 

  23. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31

    Article  Google Scholar 

  24. Hndu: Over 2,500 indian kids suffer from brain tumour every year www.thehindu.com/sci-tech/health/Over-2500-Indian-kids-suffer-from-brain-tumour-every-year/article14418512.ece (2018). Last Accessed: June 2019

  25. Hsieh TM, Liu YM, Liao CC, Xiao F, Chiang IJ, Wong JM (2011) Automatic segmentation of meningioma from non-contrasted brain mri integrating fuzzy clustering and region growing. BMC Med Inf Decis Making 11(1):54

    Article  Google Scholar 

  26. 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 

  27. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331

    Article  Google Scholar 

  28. Kim J, Feng DD, Cai TW, Eberl S (2002) Automatic 3d temporal kinetics segmentation of dynamic emission tomography image using adaptive region growing cluster analysis. In: Nuclear science symposium conference record, 2002 IEEE, vol 3. IEEE, pp 1580–1583

  29. Kistler M, Bonaretti S, Pfahrer M, Niklaus R, Büchler P (2013) The virtual skeleton database: an open access repository for biomedical research and collaboration. J Med Internet Res 15(11):e245

    Article  Google Scholar 

  30. Lun T, Hsu W (2016) Brain tumor segmentation using deep convolutional neural network. In: Proceedings of BRATS-MICCAI

  31. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R et al (2015) The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 34 (10):1993–2024

    Article  Google Scholar 

  32. Menze BH, Van Leemput K, Lashkari D, Weber MA, Ayache N, Golland P (2010) A generative model for brain tumor segmentation in multi-modal images. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 151–159

  33. Mohan G, Subashini MM (2018) Mri based medical image analysis: survey on brain tumor grade classification. Biomed Signal Process Control 39:139–161

    Article  Google Scholar 

  34. Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Comm Pure Appl Math 42(5):577–685

    Article  MathSciNet  Google Scholar 

  35. Nabizadeh N, Kubat M (2017) Automatic tumor segmentation in single-spectral mri using a texture-based and contour-based algorithm. Expert Syst Appl 77:1–10

    Article  Google Scholar 

  36. Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations. J Comput Phys 79(1):12–49

    Article  MathSciNet  Google Scholar 

  37. Pereira S, Pinto A, Alves V, Silva C (2016) Brain tumor segmentation using convolutional neural networks in mri images. IEEE Trans Med Imaging 35(5):1240–1251

    Article  Google Scholar 

  38. Pereira S, Pinto A, Alves V, Silva CA (2015) Deep convolutional neural networks for the segmentation of gliomas in multi-sequence mri. In: International workshop on brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Springer, pp 131–143

  39. 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

    Article  Google Scholar 

  40. Pratondo A, Chui CK, Ong SH (2017) Integrating machine learning with region-based active contour models in medical image segmentation. J Vis Commun Image Represent 43:1–9

    Article  Google Scholar 

  41. Ren X, Malik J (2003) Learning a classification model for segmentation. In: Null. IEEE, p 10

  42. Sachdeva J, Kumar V, Gupta I, Khandelwal N, Ahuja CK (2012) A novel content-based active contour model for brain tumor segmentation. Magn Reson Imaging 30(5):694–715

    Article  Google Scholar 

  43. Sachdeva J, Kumar V, Gupta I, et al. (2013) Segmentation, feature extraction, and multiclass brain tumor classification. J Digit Imaging 26(6):1141–1150

    Article  Google Scholar 

  44. Shivhare SN, Kumar N (2019) Brain tumor detection using manifold ranking in flair mri. In: International conference on emerging trends in information technology. Springer in press

  45. Shivhare SN, Sharma S, Singh N (2019) An efficient brain tumor detection and segmentation in mri using parameter-free clustering. In: Machine intelligence and signal analysis. Springer, pp 485–495

  46. Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X (2018) Supervised learning based multimodal mri brain tumour segmentation using texture features from supervoxels. Comput Methods Prog Biomed 157:69–84

    Article  Google Scholar 

  47. Song B, Chou CR, Chen X, Huang A, Liu MC (2016) Anatomy-guided brain tumor segmentation and classification. In: International workshop on brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Springer, pp 162–170

  48. Tong J, Zhao Y, Zhang P, Chen L, Jiang L (2019) Mri brain tumor segmentation based on texture features and kernel sparse coding. Biomed Signal Process Control 47:387–392

    Article  Google Scholar 

  49. Wang K, Ma C (2016) A robust statistics driven volume-scalable active contour for segmenting anatomical structures in volumetric medical images with complex conditions. Biomed Eng Online 15(1):39

    Article  Google Scholar 

  50. Zhao X, Wu Y, Song G, Li Z, Fan Y, Zhang Y (2016) Brain tumor segmentation using a fully convolutional neural network with conditional random fields. In: International workshop on brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Springer, pp 75–87

  51. 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

    Article  Google Scholar 

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Authors and Affiliations

  1. National Institute of Technology Uttarakhand, Srinagar, India

    Shiv Naresh Shivhare & Nitin Kumar

  2. Motilal Nehru National Institute of Technology Allahabad, Allahabad, India

    Navjot Singh

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  1. Shiv Naresh Shivhare
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Correspondence to Shiv Naresh Shivhare.

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Shivhare, S.N., Kumar, N. & Singh, N. A hybrid of active contour model and convex hull for automated brain tumor segmentation in multimodal MRI. Multimed Tools Appl 78, 34207–34229 (2019). https://doi.org/10.1007/s11042-019-08048-4

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