Abstract
The aim of this study was to establish a multi-stage fuzzy c-means (FCM) framework for the automatic and accurate detection of brain tumors from multimodal 3D magnetic resonance image data. The proposed algorithm uses prior information at two points of the execution: (1) the clusters of voxels produced by FCM are classified as possibly tumorous and non-tumorous based on data extracted from train volumes; (2) the choice of FCM parameters (e.g. number of clusters, fuzzy exponent) is supported by train data as well. FCM is applied in two stages: the first stage eliminates the most part of non-tumorous tissues from further processing, while the second stage is intended to accurately extract the tumor tissue clusters. The algorithm was tested on 13 selected volumes from the BRATS 2012 database. The achieved accuracy is generally characterized by a Dice score in the range of 0.7 to 0.9. Tests have revealed that increasing the size of the train data set slightly improves the overall accuracy.
Research supported by the Hungarian National Research Funds (OTKA), Project no. PD103921. The work of L. Lefkovits was supported by The Sectorial Operational Program Human Resources Development POSDRU/159/1.5/S/137516 financed by the European Social Found and by the Romanian Government.
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References
Gordillo, N., Montseny, E., Sobrevilla, P.: State of the art survey on MRI brain tumor segmentation. Magn. Res. Imag. 31, 1426–1438 (2013)
Asman, A.J., Landman, B.A.: Out-of-atlas labeling: a multi-atlas approach to cancer segmentation. In: 9th IEEE International Symposium on Biomedical Imaging, pp. 1236–1239. IEEE Press, New York (2012)
Ghanavati, S., Li, J., Liu, T., Babyn, P.S., Doda, W., Lampropoulos, G.: Automatic brain tumor detection in magnetic resonance images. In: 9th IEEE International Symposium on Biomedical Imaging, pp. 574–577. IEEE Press, New York (2012)
Hamamci, A., Kucuk, N., Karamam, K., Engin, K., Unal, G.: Tumor-Cut: segmentation of brain tumors on contranst enhanced MR images for radiosurgery applicarions. IEEE Trans. Med. Imag. 31, 790–804 (2012)
Sahdeva, J., Kumar, V., Gupta, I., Khandelwal, N., Ahuja, C.K.: A novel content-based active countour model for brain tumor segmentation. Magn. Res. Imag. 30, 694–715 (2012)
Njeh, I., Sallemi, L., Ayed, I.B., Chtourou, K., Lehericy, S., Galanaud, D., Hamida, A.B.: 3D multimodal MRI brain glioma tumor and edema segmentation: a graph cut distribution matching approach. Comput. Med. Imag. Graph. 40, 108–119 (2015)
Svolos, P., Tsolaki, E., Kapsalaki, E., Theodorou, K., Fountas, K., Fezoulidis, I., Tsougos, I.: Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques. Magn. Res. Imag. 31, 1567–1577 (2013)
Reddy, K.K., Solmaz, B., Yan, P., Avgeropoulos, N.G., Rippe, D.J., Shah, M.: Confidence guided enhancing brain tumor segmentation in multi-parametric MRI. In: 9th IEEE International Symposium on Biomedical Imaging, pp. 366–369. IEEE Press, New York (2012)
Yu, C.P., Ruppert, G., Collins, R., Nguyen, D., Falcao, A., Liu, Y.: 3D blob based brain tumor detection and segmentation in MR images. In: 11th IEEE International Symposium on Biomedical Imaging, pp. 1192–1197. IEEE Press, New York (2012)
Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q., Zhou, Y.: Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation. Comput. Vis. Image Underst. 115, 256–269 (2011)
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. Imag. 34(10), 1993–2024 (2015)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)
Vovk, U., Pernus̆, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imag. 26, 405–421 (2007)
Szilágyi, L., Szilágyi, S.M., Benyó, B.: Efficient inhomogeneity compensation using fuzzy \(c\)-means clustering models. Comput. Meth. Prog. Biomed. 108, 80–89 (2012)
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Szilágyi, L., Lefkovits, L., Iantovics, B., Iclănzan, D., Benyó, B. (2015). Automatic Brain Tumor Segmentation in Multispectral MRI Volumetric Records. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_21
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