References
- [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.