Visualizing human brain surface from T1-weighted MR images using texture-mapped triangle meshes.
Brain Research Unit, Low Temperature Laboratory, Helsinki University of Technology, Espoo, Finland.NeuroImage (Impact Factor: 6.36). 06/2005; 26(1):1-12. DOI: 10.1016/j.neuroimage.2005.01.030
We describe a novel method for visualizing brain surface from anatomical magnetic resonance images (MRIs). The method utilizes standard 2D texture mapping capabilities of OpenGL graphics language. It combines the benefits of volume rendering and triangle-mesh rendering, allowing fast and realistic-looking brain surface visualizations. Consequently, relatively low-resolution triangle meshes can be used while the texture images provide the necessary details. The mapping is optimized to provide good texture-image resolution for the triangles with respect to their original sizes in the 3D MRI volume. The actual 2D texture images are generated by depth integration from the original MRI data. Our method adapts to anisotropic voxel sizes without any need to interpolate the volume data into cubic voxels, and it is very well suited for visualizing brain anatomy from standard T(1)-weighted MR images. Furthermore, other OpenGL objects and techniques can be easily combined, for example, to use cut planes, to show other surfaces and objects, and to visualize functional data in addition to the anatomical information.
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ABSTRACT: In this study, four different 2D dual-tree complex wavelet (DT-CWT) based texture feature extraction methods are developed and their applications are demonstrated in segmenting and classifying tissues. Two of the methods use rotation variant texture features and the other two use rotation invariant features. This paper also proposes a novel approach to estimate 3D orientations of tissues based on rotation variant DT-CWT features. The method updates the strongest structural anisotropy direction with an iterative approach and converges to a volume orientation in few steps. Although classification and segmentation results show that there is no significant difference in the performance between rotation variant and invariant features; the latter are more robust to changes in texture rotation, which is essential for classification and segmentation of objects from 3D datasets such as medical tomography images.Data & Knowledge Engineering 12/2009; DOI:10.1016/j.datak.2009.07.009 · 1.12 Impact Factor
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ABSTRACT: Texture analysis describes a variety of image-analysis techniques that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Texture analysis may be particularly well-suited for lesion segmentation and characterization and for the longitudinal monitoring of disease or recovery. We begin this review by outlining the general procedure for performing texture analysis, identifying some potential pitfalls and strategies for avoiding them. We then provide an overview of some intriguing neuro-MR imaging applications of texture analysis, particularly in the characterization of brain tumors, prediction of seizures in epilepsy, and a host of applications to MS.American Journal of Neuroradiology 05/2010; 31(5):809-16. DOI:10.3174/ajnr.A2061 · 3.59 Impact Factor
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