3D Brain Tumors and Internal Brain Structures Segmentation in MR Images
Автор: P.NARENDRAN, V.K. NARENDIRA KUMAR, K. SOMASUNDARAM
Статья в выпуске: 1 vol.4, 2012 года.
The main topic of this paper is to segment brain tumors, their components (edema and necrosis) and internal structures of the brain in 3D MR images. For tumor segmentation we propose a framework that is a combination of region-based and boundary-based paradigms. In this framework,segment the brain using a method adapted for pathological cases and extract some global information on the tumor by symmetry based histogram analysis. We propose a new and original method that combines region and boundary information in two phases: initialization and refinement. The method relies on symmetry-based histogram analysis.The initial segmentation of the tumor is refined relying on boundary information of the image. We use a deformable model which is again constrained by the fused spatial relations of the structure. The method was also evaluated on 10 contrast enhanced T1-weighted images to segment the ventricles,caudate nucleus and thalamus.
3D, Brain, Tumor, Segmentation, MRI, Image Registration, and Brain Structures.
Короткий адрес: https://readera.ru/15012207
Список литературы 3D Brain Tumors and Internal Brain Structures Segmentation in MR Images
- Algorri, M. E. and Flores-Mangas, F. (2004). Classification of anatomical structures in MR brain images using fuzzy parameters. IEEE Transactions on Biomedical Engineering, 51(9):1599–1608.
- Dou, W., Ruan, S., Chen, Y., Bloyet, D., and Constans, J. M. (2007). A framework of fuzzy information fusion for segmentation of brain tumor tissues on MR images. Image and Vision Computing, 25:164–171.
- Fletcher-Heath, L. M., Hall, L. O., Goldgof, D. B., and Murtagh, F. (2001). Automatic segmentation of non-enhancing brain tumor in magnetic resonance images. Artificial Intelligence in Medicine, 21:43–63.
- Gering, D. T. (2003). Recognizing Deviations from Normalcy for Brain Tumor Segmentation. PhD thesis, Massachusetts Institute of Technology.
- Hata, N., Muragaki, Y., Inomata, T., Maruyama, T., Iseki, H., Hori, T., and Dohi, T. (2005). Interaoperative tumor segmentation and volume measurement in MRI guided glioma surgery for tumor resection rate control. Academic Radiology, 12:116–122.
- Hu, S. and Collins, D. L. (2007). Joint level-set shape modeling and appearance modeling for brain structure segmentation. NeuroImage, 36:672–683.
- K. M., Jia, W., and Marsh, R. (2003). Fractal analysis of tumor in brain MR images. Machine Vision and Applications, 13:352–362.
- Khotanlou, H., Colliot, O., and Bloch, I. (2007). Automatic Brain Tumor Segmentation using Symmetry Analysis and Deformable Models. In International Conference on Advances in Pattern Recognition (ICAPR), pages 198–202, Kolkata, India.
- Lefohn, A., Cates, J., and Whitaker, R. (2003). Interactive, GPU-based level sets for 3D brain tumor segmentation. Technical report, University of Utah.
- Maintz, J. and Viergever, M. (1998). A survey of medical image registration. Medical Image Analysis, 2(1):1–36.