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Glioma extraction from MR images employing Gradient Based Kernel Selection Graph Cut technique

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Abstract

Medical imaging is one of the most daunting, challenging, and emerging research topics in image processing. Segmenting the glioma from the brain magnetic resonance images (MRI) is an important and demanding task, as it assists the medical experts for the disease diagnosis process. Recent research methods in image segmentation have highlighted the prospective of graph-based techniques for medical applications. As graph cut (GC) method is interactive in nature, it requires manual selection of the initial kernels for processing. The popularity of the GC method is limited by the occurrence of small cuts due to its shrinkage behavior leading to inaccurate extraction causing erroneous regions. This paper addresses the open research issue of shrinkage behavior by proposing the gradient based kernel selection (GBKS) GC method emphasizing on the directive inclination of the intensity scales. The proposed technique aids in the initialization of GC, removes the shrinkage problem, and locates the tumor in brain images without any human intervention. The performance results of the proposed GBKS GC method are evaluated on high-grade glioma and low-grade glioma MRI images and are analyzed and compared by using various measures. All the results present a remarkable improvement with GBKS GC technique over other existing techniques.

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References

  1. Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.: A survey of MR-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58, R97 (2013)

    Google Scholar 

  2. Benhabiles, H., Vandeborre, J.P., Lavoué, G., Daoudi, M.: A comparative study of existing metrics for 3D-mesh segmentation evaluation. Vis. Comput. 26(12), 1451–1466 (2010)

    Google Scholar 

  3. Pan, R., Taubin, G.: Automatic segmentation of point clouds from multi-view reconstruction using graph-cut. Vis. Comput. 32(5), 601–609 (2016)

    Google Scholar 

  4. Cuadros Linares, O., Bianchi, J., Raveli, D., Batista Neto, J., Hamann, B.: Mandible and skull segmentation in cone beam computed tomography using super-voxels and graph clustering. Vis. Comput. (2018). https://doi.org/10.1007/s00371-018-1511-0

    Article  Google Scholar 

  5. Hanaoka, S., Fritscher, K., Welk, M., Nemoto, M., Masutani, Y., Hayashi, N., et al.: 3-d graph cut segmentation with riemannian metrics to avoid the shrinking problem. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 554–561 (2011)

  6. Madabhushi, A., Metaxas, D.,: Automatic boundary extraction of ultrasonic breast lesions. In: Proceedings of the IEEE International Symposium in Biomedical Imaging, pp. 601–604 (2002)

  7. Shan, J., Cheng, H. D., Wang, Y.: A novel automatic seed point selection algorithm for breast ultrasound images. In: Proceedings of the International Conference on Pattern Recognition, pp. 1–4 (2008)

  8. Shan, J., Cheng, H., Wang, Y.: Completely automated segmentation approach for breast ultrasound images using multiple-domain features. Ultrasound Med. Biol. 38, 262–275 (2012)

    Google Scholar 

  9. Peng, J., Shen, J., Li, X.: High-order energies for stereo segmentation. IEEE Trans. Cybern. 46(7), 1616–1627 (2016)

    Google Scholar 

  10. Ishikawa, H.: Higher-order clique reduction in binary graph cut. In: Proceedings of the IEEE CVPR, pp. 2993–3000 (2009)

  11. Rother, C., Kohli, P., Feng, W., Jia, J.: Minimizing sparse higher order energy functions of discrete variables. In: Proceedings of the IEEE CVPR, pp. 1382–1389 (2009)

  12. Vicente, S., Kolmogorov, V., Rother, C.: Joint optimization of segmentation and appearance models. In: Proceedings of the IEEE ICCV, pp. 755–762 (2009)

  13. Shen, J., Peng, J., Dong, X., Shao, L., Porikli, F.: Higher order energies for image segmentation. IEEE Trans. Image Process. 26(10), 4911–4922 (2017)

    MathSciNet  MATH  Google Scholar 

  14. Wang, W., Shen, J.: Higher-order image co-segmentation. IEEE Trans. Multimed. 18(6), 1011–1021 (2016)

    Google Scholar 

  15. Jermyn, I.H., Ishikawa, H.: Globally optimal regions and boundaries as minimum ratio weight cycles. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1075–1088 (2001)

    Google Scholar 

  16. Sinop, A. K., Grady, L.: A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: Proceedings of the IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)

  17. Kolmogorov, V., Boykov, Y.: What metrics can be approximated by geo-cuts, or global optimization of length/area and flux. In: Proceedings of the 10th IEEE International Conference on Computer Vision, pp. 564–571 (2005)

  18. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001)

    Google Scholar 

  19. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000)

    Google Scholar 

  20. Hao. Z., Wang, Q., Ren, H., Xu, K., Seong, Y. K., Kim, J.: Multiscale superpixel classification for tumor segmentation in breast ultrasound images. In: 19th IEEE International Conference on Image Processing (ICIP), pp. 2817–2820 (2012)

  21. Gao, L., Yang, W., Liao, Z., Liu, X., Feng, Q., Chen, W.: Segmentation of ultrasonic breast tumors based on homogeneous patch. Med. Phys. 39, 3299–3318 (2012)

    Google Scholar 

  22. Alemán-Flores, M., Álvarez, L., Caselles, V.: Texture-oriented anisotropic filtering and geodesic active contours in breast tumor ultrasound segmentation. J. Math. Imaging Vis. 28, 81–97 (2007)

    MathSciNet  Google Scholar 

  23. Liu, X., Huo, Z., Zhang. J.: Automated segmentation of breast lesions in ultrasound images. In: Proceedings of the 27th Annual International Conference Engineering in Medicine and Biology Society, pp. 7433–7435 (2005)

  24. Averbuch-Elor, H., Kopf, J., Hazan, T., Cohen-Or, D.: Co-segmentation for space-time co-located collections. Vis. Comput. 34(12), 1761–1772 (2018)

    Google Scholar 

  25. Wan, C., Yuan, B., Miao, Z.: Markerless human body motion capture using Markov random field and dynamic graph cuts. Vis. Comput. 24(5), 373–380 (2008)

    Google Scholar 

  26. Jiang, J., Wu, Y., Huang, M., Yang, W., Chen, W., Feng, Q.: 3D brain tumor segmentation in multimodal MR images based on learning population-and patient-specific feature sets. Comput. Med. Imaging Graph. 37, 512–521 (2013)

    Google Scholar 

  27. Zhao, L., Sarikaya, D., Corso, J.J.: Automatic brain tumor segmentation with MRF on supervoxels. Multimodal Brain Tumor Segm. 51, 54–57 (2013)

    Google Scholar 

  28. Szwarc, P., Kawa, J., Rudzki, M., Pietka, E.: Automatic brain tumour detection and neovasculature assessment with multiseries MR analysis. Comput. Med. Imaging Graph. 46, 178–190 (2015)

    Google Scholar 

  29. Popuri, K., Cobzas, D., Murtha, A., Jägersand, M.: 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set. Int. J. Comput. Assist. Radiol. Surg. 7(4), 493–506 (2012)

    Google Scholar 

  30. Kanas, V.G., Zacharaki, E.I., Davatzikos, C., Sgarbas, K.N., Megalooikonomou, V.: A low cost approach for brain tumor segmentation based on intensity modeling and 3D Random Walker. Biomed. Signal Process. Control 22, 19–30 (2015)

    Google Scholar 

  31. Ilunga-Mbuyamba, E., Avina-Cervantes, J.G., Garcia-Perez, A., de Romero-Troncoso, R.J., Aguirre-Ramos, H., Cruz-Aceves, I., et al.: Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation. Neurocomputing 220, 84–97 (2017)

    Google Scholar 

  32. Shen, J., Hao, X., Liang, Z., Liu, Y., Wang, W., Shao, L.: Real-time superpixel segmentation by DBSCAN clustering algorithm. IEEE Trans. Image Process. 25(12), 5933–5942 (2016)

    MathSciNet  MATH  Google Scholar 

  33. Yuan, C., Qin, X., Qin, Z., Wang, R.: Image segmentation based on modified superpixel segmentation and spectral clustering. J. Eng. 16, 1704–1711 (2018)

    Google Scholar 

  34. Shen, J., Du, Y., Wang, W., Li, X.: Lazy random walks for superpixel segmentation. IEEE Trans. Image Process. 23(4), 1451–1462 (2014)

    MathSciNet  MATH  Google Scholar 

  35. Dong, X., Shen, J., Shao, L., Gool, L.V.: Sub-Markov random walk for image segmentation. IEEE Trans. Image Process. 25(2), 516–527 (2016)

    MathSciNet  MATH  Google Scholar 

  36. Shen, J., Peng, J., Shao, L.: Submodular trajectories for better motion segmentation in videos. IEEE Trans. Image Process. 27(6), 2688–2700 (2018)

    MathSciNet  MATH  Google Scholar 

  37. Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., et al. Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Proceedings of the Medical Image Computing and Computer-Assisted Intervention MICCAI-BRATS, pp. 369–376 (2012)

  38. Tustison, N., Wintermark, M., Durst, C., Brian, A., ANTs and Arboles. In: Proceedings of the BRATS-MICCAI (2013)

  39. Ledig, C., Heckemann, R.A., Hammers, A., Lopez, J.C., Newcombe, V.F., Makropoulos, A., et al.: Robust whole-brain segmentation application to traumatic brain injury. Med. Image Anal. 21(1), 40–58 (2015)

    Google Scholar 

  40. Li, R., Zhang, W., Suk, H.-I., Wang, L., Li, J., Shen, D., Ji, S.: Deep learning based imaging data completion for improved brain disease diagnosis. In: Proceedings of the Medical Image Computing and Computer-Assisted Intervention MICCAI-BRATS, pp. 305–312 (2014)

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

    Google Scholar 

  42. Schmidt, P., Gaser, C., Arsic, M., Buck, D., Forschler, A., Berthele, A., Hoshi, M., et al.: An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 59(4), 3774–3783 (2012)

    Google Scholar 

  43. Liu, X., Niethammer, M., Kwitt, R., McCormick, M., Aylward, S.: Low-rank to the rescue–atlas-based analyses in the presence of pathologies. In: Proceedings of the Medical Image Computing and Computer-Assisted Intervention MICCAI-BRATS, pp. 97–104 (2014)

  44. Ye, D. H., Zikic, D., Glocker, B., Criminisi, A., Konukoglu, E.: Modality propagation: coherent synthesis of subject-specific scans with data-driven regularization. In: Proceedings of the Medical Image Computing and Computer Assisted Intervention MICCAI-BRATS. pp. 606–613 (2013)

  45. Li, Y., Jia, F., Qin, J.: Brain tumor segmentation from multimodal magnetic resonance images via sparse representation. Artif. Intell. Med. 73, 1–13 (2016)

    Google Scholar 

  46. Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)

    Google Scholar 

  47. Zikic, D., Ioannou, Y., Brown, M., Criminisi, A.: Segmentation of brain tumor tissues with convolutional neural networks. In: Proceedings of the Medical Image Computing and Computer-Assisted Intervention MICCAI-BRATS, pp. 36–39 (2014)

  48. Li, X., Huang, H., Zhao, H., Wang, Y., Hu, M.: Learning a convolutional neural network for propagation-based stereo image segmentation. Vis. Comput. 99, 1–14 (2018)

    Google Scholar 

  49. Zheng, C., Wang, J., Chen, W., Wu, X.: Multi-class indoor semantic segmentation with deep structured model. Vis. Comput. 2018, 1–13 (2018)

    Google Scholar 

  50. Wang, W., Shen, J., Shao, L.: Video salient object detection via fully convolutional networks. IEEE Trans. Image Process. 27(1), 38–49 (2018)

    MathSciNet  MATH  Google Scholar 

  51. Wang, W., Shen, J.: Deep visual attention prediction. IEEE Trans. Image Process. 27(5), 2368–2378 (2018)

    MathSciNet  Google Scholar 

  52. Tustison, N., Shrinidhi, J., Wintermark, K.L., Durst, M., Kandel, C.R., Gee, B.M., et al.: Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13(2), 209–225 (2015)

    Google Scholar 

  53. Bi, L., Feng, D., Kim, J.: Dual-path adversarial learning for fully convolutional network (FCN)-based medical image segmentation. Vis. Comput. 34, 1–10 (2018)

    Google Scholar 

  54. Bi, L., Kim, J., Kumar, A., Fulham, M., Feng, D.: Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation. Vis. Comput. 33(6–8), 1061–1071 (2017)

    Google Scholar 

  55. Li, X., Huang, H., Zhao, H., Wang, Y., Hu, M.: Learning a convolutional neural network for propagation-based stereo image segmentation. Vis. Comput. (2018). https://doi.org/10.1007/s00371-018-1582-y

    Article  Google Scholar 

  56. Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)

    Google Scholar 

  57. Chen, X., Pan, L.: A survey of graph cuts/graph search based medical image segmentation. IEEE Rev. Biomed. Eng. 11, 112–124 (2018)

    Google Scholar 

  58. Boykov, Y. Y., Jolly, M. P.: Interactive graph cuts for optimal boundary and region segmentation of objects in ND images. In: Proceedings of the 8th IEEE International Conference on Computer Vision, pp. 105–112 (2001)

  59. Rother, C., Kolmogorov, V., Blake, A.: Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph (TOG) 23(3), 309–314 (2004)

    Google Scholar 

  60. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., et al.: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)

    Google Scholar 

  61. Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34, 1993–2024 (2015)

    Google Scholar 

  62. Menze, B. H., Van Leemput, K., Lashkari, D., Weber, M.A., Ayache, N., Golland, P.: A generative model for brain tumor segmentation in multi-modal images. In: Proceeding of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 151–159 (2010)

  63. Rao, B. D., Goswami, M. M.: A comprehensive study of features used for brian tumor detection and segmentation from MR images. In: Proceeding of the Power and Advanced Computing Technologies (i-PACT), Innovations, pp. 1–6 (2017)

  64. Rosenfeld, A., Pfaltz, J.L.: Sequential operations in digital picture processing. J. ACM 13, 471–494 (1966)

    MATH  Google Scholar 

  65. Park, C., Huang, J.Z., Ji, J.X., Ding, Y.: Segmentation, inference and classification of partially overlapping nanoparticles. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1 (2013)

    Google Scholar 

  66. Dogra, J., Jain, S., Sood, M.: Segmentation of MR images using hybrid kMean-graph cut technique. Proc. Comput. Sci. 132, 775–784 (2018)

    Google Scholar 

  67. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient ND image segmentation. Int. J. Comput. Vis. 70, 109–131 (2006)

    Google Scholar 

  68. Grady, L., Schiwietz, T., Aharon, S., Westermann, R.: Random walks for interactive organ segmentation in two and three dimensions: Implementation and validation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 773–780 (2005)

  69. Despotović, I., Goossens, B., Philips, W.: MRI segmentation of the human brain: challenges, methods, and applications. Comput. Math. Methods Med. 2015, 23 (2015)

    Google Scholar 

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

    Google Scholar 

  71. Sobhaninia, Z., Rezaei, S., Noroozi, A., Ahmadi, M., Zarrabi, H., Karimi, N., Emami, A., Samavi, S.: Brain tumor segmentation using deep learning by type specific sorting of images. arXiv preprint arXiv:1809.07786 (2018)

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Acknowledgements

We would like to express our appreciation to radiologist in Indra Gandhi Medical Hospital (IGMC), Shimla, India, for his guidance and assistance in providing us database. His expert guidance aided us in validating our results.

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  1. Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Jyotsna Dogra, Shruti Jain & Meenakshi Sood

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  1. Jyotsna Dogra
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Correspondence to Jyotsna Dogra.

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Dogra, J., Jain, S. & Sood, M. Glioma extraction from MR images employing Gradient Based Kernel Selection Graph Cut technique. Vis Comput 36, 875–891 (2020). https://doi.org/10.1007/s00371-019-01698-3

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