Asian Journal of Computer Science and Technology (AJCST)
Segmentation of 3D Brain Structures in MRI ImagesAuthor : P. Narendran and V. Thiagarasu
Volume 8 No.2 April-June 2019 pp 13-18
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, we first segment the brain using a method adapted for pathological cases and extract some global information on the tumor by symmetry based histogram analysis. The second step segments the tumor and its components. For this, we propose a new and original method that combines region and boundary information in two phases: initialization and refinement. For initialization, which is mostly region-based, we present two new methods. The first one is a new fuzzy classification method which combines the membership, typicality and neighborhood information of the voxels. The second one relies on symmetry-based histogram analysis. The initial segmentation of the tumor is refined relying on boundary information of the image. This method is a deformable model constrained by spatial relations. The spatial relations are obtained based on the initial segmentation and surrounded tissues of the tumor. The proposed method can be used for a large class of tumors in any modality of MR images. To segment a tumor and its components automatically the proposed framework needs only a contrast enhanced T1-weighted image and a FLAIR image. In the case of a contrast enhanced T1-weighted image only, some user interaction will be needed. To refine the initial segmentation, we use a deformable model which is again constrained by the fused spatial relations of the structure. This 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 Structure
 M. E. Algorri, and F. Flores-Mangas, “Classification of anatomical structures in MR brain images using fuzzy parameters” IEEE Transactions on Biomedical Engineering, Vol. 51, No. 9 , pp. 1599–1608, 2004.
 W. Dou, S. Ruan, Y. Chen, D. Bloyet, and J. M. Constans, “A framework of fuzzy information fusion for segmentation of brain tumor tissues on MR images”, Image and Vision Computing, Vol. 25, pp. 164–171, 2007.
 L. M. Fletcher- L. O. HeathHall, D. B. Goldgof, and F. Murtagh, “Automatic segmentation of non-enhancing brain tumor in magneticresonance images”, Artificial Intelligence in Medicine, Vol. 21, pp. 43–63, 2001.
 D.T. Gering, “Recognizing Deviations from Normalcy for Brain Tumor Segmentation”, PhD thesis, Massachusetts Institute of Technology, 2003.
 N. Hata,Y. Muragaki, T. Inomata, T. Maruyama, H. Iseki, T. Hori, and T. Dohi, “Intera-operative tumor segmentation and Vol. measurement in MRI guided glioma surgery for tumor resection rate control”, Academic Radiology, Vol. 12, pp. 116–122, 2005.
 S. Hu, and D. L. Collins, “Joint level-set shape modeling and appearance modeling for brain structure segmentation”, Neuro Image, Vol. 36, pp. 672–683, 2007.
 K. M. Iftekharuddin, W. Jia, and R. Marsh, “Fractal analysis of tumor in brain MR images”, Machine Vision and Applications, Vol. 13, pp. 352–362, 2003.
 H. Khotanlou, O. Colliot, and I. Bloch, “Automatic Brain Tumor Segmentation using Symmetry Analysis and Deformable Models”, In International Conference on Advances in Pattern Recognition, pp. 198–202, Kolkata, India, 2007.
 A. Lefohn, J. Cates, and R. Whitaker, “Interactive, GPU-based level sets for 3D brain tumor segmentation”, Technical report, University of Utah, 2003.
 J. Maintz, and M. Viergever, “A survey of medical image registration”, Medical Image Analysis, Vol. 2, No. 1, pp. 1–3, 1998.