Abstract
Brain tumors vary considerably in size, morphology, and location across patients, thus pose great challenge in automated brain tumor segmentation methods. Inspired by the concept of clique in graph theory, we present a clique-based method for multimodal brain tumor segmentation that considers a brain tumor image as a graph and automatically segment it into different sub-structures based on the clique homogeneity. Our proposed method has three steps, neighborhood construction, clique identification, and clique propagation. We constructed the neighborhood of each pixel based on its similarities to the surrounding pixels, and then extracted all cliques with a certain size k to evaluate the correlations among different pixels. The connections among all cliques were represented as a transition matrix, and a clique propagation method was developed to group the cliques into different regions. This method is also designed to accommodate multimodal features, as multimodal neuroimaging data is widely used in mapping the tumor-induced changes in the brain. To evaluate this method, we conduct the segmentation experiments on the publicly available Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) dataset. The qualitative and quantitative results demonstrate that our proposed clique-based method achieved better performance compared to the conventional pixel-based methods.
Keywords
- Support Vector Machine
- Segmentation Result
- Local Binary Pattern
- Support Vector Machine Classifier
- Local Patch
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Holland, E.C.: Progenitor cells and glioma formation. Curr. Opin. Neurol. 14, 683–688 (2001)
Angelini, E.D., Clatz, O., Mandonnet, E., Konukoglu, E., Capelle, L., Duffau, H.: Glioma dynamics, computational models,: a review of segmentation, registration, and in silico growth algorithms and their clinical applications. Curr. Med. Imaging Rev. 3(4), 262–276 (2007)
Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.: A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), 97–129 (2013)
Liu, S., Cai, W., Liu, S.Q., Zhang, F., Fulham, M.J., Feng, D., et al.: Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Inform. 2(3), 167–180 (2015)
Liu, S., Cai, W., Liu, S.Q., Zhang, F., Fulham, M.J., Feng, D., et al.: Multimodal neuroimaging computing: the workflows, methods, and platforms. Brain Inform. 2(3), 181–195 (2015)
Menze, B., 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(10), 1993–2024 (2014)
Corso, J.J., Sharon, E., Dube, S., El-Saden, S., Sinha, U., Yuille, A.: Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Trans. Med. Imaging 27(5), 629–640 (2008)
Pohl, K.M., Fisher, J., Levitt, J.J., Shenton, M.E., Kikinis, R., Grimson, W.E.L., Wells, W.M.: A unifying approach to registration, segmentation, and intensity correction. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 310–318. Springer, Heidelberg (2005). doi:10.1007/11566465_39
Parisot, S., Duffau, H., Chemouny, S., Paragios, N.: Graph-based detection, segmentation & characterization of brain tumors. In: CVPR, pp. 988–995. IEEE (2012)
Wels, M., Carneiro, G., Aplas, A., Huber, M., Hornegger, J., Comaniciu, D.: A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5241, pp. 67–75. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85988-8_9
Pohl, K.M., Bouix, S., Kikinis, R., Grimson, W.E.L.: Anatomical guided segmentation with non-stationary tissue class distributions in an expectation-maximization framework. In: ISBI, pp. 81–84. IEEE (2004)
Liu, S., Cai, W., Wen, L., Eberl, S., Fulham, M.J., Feng, D.: A robust volumetric feature extraction approach for 3D neuroimaging retrieval. In: EMBC, pp. 5657–5660. IEEE (2010)
Cai, W., Liu, S., Song, Y., Pujol, S., Kikinis, R., Feng, D.: A 3D difference-of-Gaussian-based lesion detector for brain PET. In: ISBI, pp. 677–680. IEEE (2014)
Liu, S., Jing, L., Cai, W., Wen, L., Eberl, S., Fulham, M.J., et al.: Localized multiscale texture based retrieval of neurological image. In: CBMS, pp. 243–248. IEEE (2010)
Liu, S., Cai, W., Wen, L., Eberl, S., Fulham, M.J., Feng, D.: Localized functional neuroimaging retrieval using 3D discrete curvelet transform. In: ISBI, pp. 1877–1880. IEEE (2011)
Ng, G., Song, Y., Cai, W., Zhou, Y., Liu, S., Feng, D.: Hierarchical and binary spatial descriptors for lung nodule image retrieval. In: EMBC, pp. 6463–6466. IEEE (2014)
Liu, S., Cai, W., Wen, L., Feng, D.: Volumetric congruent local binary patterns for 3D neurological image retrieval. In: International Conference on Image and Vision Computing New Zealand, pp. 272–276 (2011)
Liu, S., Cai, W., Wen, L., Feng, D.: Multiscale and multiorientation feature extraction with degenerative patterns for 3D neuroimaging retrieval. In: ICIP, pp. 1249–1252. IEEE (2012)
Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation 1. Ann. Rev. Biomed. Eng. 2, 315–337 (2000)
Li, S.Z., Singh, S.: Markov Random Field Modeling in Image Analysis. Springer, London (2009)
Subbanna, N.K., Precup, D., Collins, D.L., Arbel, T.: Hierarchical probabilistic Gabor and MRF segmentation of brain tumours in MRI volumes. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 751–758. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40811-3_94
Bauer, S., Nolte, L.-P., Reyes, M.: Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 354–361. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23626-6_44
West, D.B.: Introduction to Graph Theory. Prentice Hall, Upper Saddle River (2001)
Zhang, F., Cai, W., Song, Y., Young, P., Traini, D., Morgan, L., et al.: Beating cilia identification in fluorescence microscope images for accurate CBF measurement. In: ICIP, 4496–4500. IEEE (2015)
Weizman, L., Sira, L.B., Joskowicz, L., Constantini, S., Precel, R., Shofty, B., et al.: Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI. Med. Image Anal. 16(1), 177–188 (2012)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: The Fifth Annual Workshop on Computational Learning Theory, pp. 144–152 (1992)
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Liu, S., Song, Y., Zhang, F., Feng, D., Fulham, M., Cai, W. (2016). Clique Identification and Propagation for Multimodal Brain Tumor Image Segmentation. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_28
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