Unsupervised Pathological Area Extraction using 3D T2 and FLAIR MR Images

Pavel Dvořák 1 , 2 , Karel Bartušek 2 ,  and Zdeněk Smékal 1
  • 1 Deptartment of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 612 00 Brno, Czech Republic.
  • 2 ASCR, Institute of Scientific Instruments, Kralovopolska 147, 612 64 Brno, Czech Republic


This work discusses fully automated extraction of brain tumor and edema in 3D MR volumes. The goal of this work is the extraction of the whole pathological area using such an algorithm that does not require a human intervention. For the good visibility of these kinds of tissues both T2-weighted and FLAIR images were used. The proposed method was tested on 80 MR volumes of publicly available BRATS database, which contains high and low grade gliomas, both real and simulated. The performance was evaluated by the Dice coefficient, where the results were differentiated between high and low grade and real and simulated gliomas. The method reached promising results for all of the combinations of images: real high grade (0.73 ± 0.20), real low grade (0.81 ± 0.06), simulated high grade (0.81 ± 0.14), and simulated low grade (0.81 ± 0.04).

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1] Gogola, D., Krafcik, A., Strbak, O., Frollo, I. (2013). Magnetic resonance imaging of surgical implants made from weak magnetic materials. Measurement Science Review 13(4), 165–168.

  • [2] Ahlgren, A., Wirestam, R., Stahlberg, F., Knutsson, L. (2014). Automatic brain segmentation using fractional signal modeling of a multiple flip angle, spoiled gradient-recalled echo acquisition. Magnetic Resonance Materials in Physics, Biology and Medicine.

  • [3] Mikulka, J., Gescheidtova, E. (2013). An improved segmentation of brain tumor, edema and necrosis. In: Progress in Electromagnetics Research Symposium. pp. 25–28.

  • [4] Wu, Y., Yang, W., Jiang, J., Li, S., Feng, Q., Chen, W. (2013). Semi-automatic segmentation of brain tumors using population and individual information. Journal of Digital Imaging 26(4), 786–796.

  • [5] Pedoia, V., Binaghi, E., Balbi, S., De Benedictis, A., Monti, E., Minotto, R. (2012). Glial brain tumor detection by using symmetry analysis. In: Proc. SPIE, Vol. 8314. pp. 831445–831445–8.

  • [6] Saha, B. N., Ray, N., Greiner, R., Murtha, A., Zhang, H. (2012). Quick detection of brain tumors and edemas: A bounding box method using symmetry. Computerized Medical Imaging and Graphics 36(2), 95–107.

  • [7] Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q., Zhu, Y. (2011). Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation. Computer Vision and Image Understanding 115(2), 256–269.

  • [8] Corso, J. J., Sharon, E., Yuille, A. (2006). A.: Multilevel segmentation and integrated bayesian model classification with an application to brain tumor segmentation. In: Medical Image Computing and Computer Assisted Intervention. pp. 790–798.

  • [9] Ho, S., Bullitt, E., Gerig, G. (2002). Level-set evolution with region competition: Automatic 3-D segmentation of brain tumors. In: Proceedings of the 16 th International Conference on Pattern Recognition (ICPR’02) Volume 1. Washington, DC, USA, 10532.

  • [10] Mikulka, J., Gescheidtova, E., Bartusek, K. (2012). Soft-tissues image processing: Comparison of traditional segmentation methods with 2D active contour methods. Measurement Science Review 12(4), 153–161.

  • [11] Cap, M., Gescheidtova, E., Marcon, P., Bartusek, K. (2013). Automatic detection and segmentation of the tumor tissue. In: Progress in Electromagnetics Research Symposium. pp. 53–56.

  • [12] A. Rajendran, Dhanasekaran, R. (2012). Fuzzy clustering and deformable model for tumor segmentation on MRI brain image: A combined approach. In: Procedia Engineering, International Conference on Communication Technology and System Design 2011, 30, pp. 327– 333.

  • [13] Benes, R., Karasek, J., Burget, R., Riha, K. (2013). Automatically designed machine vision system for the localization of CCA transverse section in ultrasound images. Computer Methods and Programs in Biomedicine 109(1), 92–103.

  • [14] Islam, A., Reza, S. M. S., Iftekharuddin, K. M. (2013). Multifractal texture estimation for detection and segmentation of brain tumors. IEEE Trans Biomed Eng. 60 (11), 3204–3215.

  • [15] Zhao, L., Wu, W., Corso, J. J. (2013). Semi-automatic brain tumor segmentation by constrained MRFS using structural trajectories. In: Medical Image Computing and Computer-Assisted Intervention MICCAI 2013, Lecture Notes in Computer Science Volume, Vol. 8151. pp. 567–575.

  • [16] Capelle, A.-S., Colot, O., Fernandez-Maloigne, C. (2004). Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information. Information Fusion 5(3), 203–216.

  • [17] Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., Gerig, G. (2003). Automatic brain tumor segmentation by subject specific modification of atlas priors. Academic Radiology 10 (12), 1341–1348.

  • [18] Otsu, N. (1979). A Threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66.

  • [19] Dvorak, P., Kropatsch, W. G., Bartusek, K. (2013). Automatic brain tumor detection in T2- weighted magnetic resonance images. Measurement Science Review 13(5), 223–230.

  • [20] Uher, V., Burget, R., Masek, J., Dutta, M. (2013). 3D brain tissue selection and segmentation from MRI. In: Telecommunications and Signal Processing (TSP), 2013 36th International Conference on. pp. 839–842.

  • [21] Ruppert, G. C. S., Teverovskiy, L., Yu, C., Falcao, A. X., Liu, Y. (2011). A new symmetry-based method for midsagittal plane extraction in neuroimages. In: International Symposium on Biomedical Imaging: From Macro to Nano.

  • [22] Bhattacharyya, A. (1943). On a measure of divergence between two statistical populations defined by their probability distribution. Bulletin of the Calcutta Mathematical Society 35, 99–110.

  • [23] Kropatsch, W. G., Haxhimusa, Y., Ion, A. (2007). Multiresolution image segmentations in graph pyramids. Applied Graph Theory in Computer Vision and Pattern Recognition Studies in Computational Intelligence. 52, 3–41.

  • [24] Dvorak, P., Bartusek, K., Kropatsch, W. G. (2013). Automated segmentation of brain tumor edema in FLAIR MRI using symmetry and thresholding. In: Progress in Electromagnetics Research Symposium. pp. 936–939.

  • [25] Cocosco, C. A., Kollokian, V., Kwan, R. K.-S., Pike, G. B., Evans, A. C. (1997). Brainweb: Online interface to a 3D MRI simulated brain database. NeuroImage 5, 425.

  • [26] Prastawa, M., Bullitt, E., Gerig, G. (2009). Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Medical Image Analysis 13(2), 297–311.

  • [27] Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology 26(3), 297–302.

  • [28] Menze, B., Jakab, A., Bauer, S. et al. (2014). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers (IEEE), pp. 33. http://hal.inria.fr/hal-00935640.


Journal + Issues