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Proceedings of CAD'16, 2016, 348-351
Progressive Extraction of Neural Models from High-resolution 3D Images of Brain
Abstract. In the last few years, the fast progresses of scanning technologies have produced a great increment of resolution of 2D/3D images, measured by the number of distinct pixels in each dimension that can be displayed on a display device. This growth of resolution is about one order of magnitude (10 times) in each single spatial dimension, and therefore of second order in the number of pixels in 2D and of order three for the number of 3D voxels. Hence, the sectional image of a neuronal or vessel structure in the brain, depicted in the past - at maximum resolution - by few pixels in its average section, say less than 10x10, is currently reproduced by the newest imaging technologies as measuring more than ten times that number. In the past years, aiming to accurately describe a curved shape, we needed to consider the voxels as the elements of a discrete intensity field, and to compute a triangulated equi-surface of the field, in order to get some adequately approximated boundary representation of the studied structures. The well-known “marching cubes” algorithm was the typical computational method used for this purpose, together with its several variants.
Keywords. 3D medical imaging, Neural images, Progressive imaging, Model extraction, ViSUS, LAR, Linear Algebraic Representation