Abstract
An algorithm is presented for fully automated brain tumor segmentation from only two magnetic resonance image modalities. The technique is based on three steps: (1) alternating different levels of automatic histogram-based multi-thresholding step, (2) performing an effective and fully automated procedure for skull-stripping by evolving deformable contours, and (3) segmenting both Gross Tumor Volume and edema. The method is tested using 19 hand-segmented real tumors which shows very accurate results in comparison to a very recent method (STS) in terms of the Dice coefficient. Improvements of 5% and 20% respectively for segmentation of edema and Gross Tumor Volume have been recorded.
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References
Diaz, I., Boulanger, P., Greiner, R., Hoehn, B., Rowe, L., Murtha, A.: An Automatic Brain Tumor Segmentation Tool. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan, July 3-7 (2013)
Mazzara, G.P., Velthuizen, R.P., Pearlman, J.L., Greenberg, H.M., Wagner, H.: Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. Int. J. Radiat. Oncol. Biol. Phys. 59 (2004)
Schmidt, M.: Automatic brain tumor segmentation. M.sc. thesis, University of Alberta (2005)
Brummer, M.E., Mersereau, R.M., Eisner, R.L., Lewine, R.R.J.: Automatic detection of brain contours in MRI data sets. IEEE Transactions on Medical Imaging 12(2), 153–166 (1993)
Savitzky, A., Golay, M.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chemm. 36 (1964)
Pham, D., Xu, C., Prince, J.: Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2 (2000)
Bankman, I.: Handbook of Medical image: processing and Analysis (2008)
Wang, Y., Nie, J., Yap, P.-T., Shi, F., Guo, L., Shen, D.: Robust deformable-surface-based skull-stripping for large-scale studies. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 635–642. Springer, Heidelberg (2011)
Tao, X., Chang, M.-C.: A skull stripping method using deformable surface and tissue classification. SPIE (2010)
Li, C., Xu, C., Gui, C., Fox, M.D.: Level Set Evolution without Re-Initialization: A New Variational Formulation. In: CVPR (2005)
Salah, M.B., Mitiche, A., Ben Ayed, I.: A continuous labeling for multiphase graph cut image partitioning. In: Bebis, G., et al. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 268–277. Springer, Heidelberg (2008)
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Ben Salah, M., Diaz, I., Greiner, R., Boulanger, P., Hoehn, B., Murtha, A. (2013). Fully Automated Brain Tumor Segmentation Using Two MRI Modalities. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41914-0_4
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DOI: https://doi.org/10.1007/978-3-642-41914-0_4
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