NDLI: Towards real-time visualization of detailed neural tissue models: View frustum culling for parallel rendering

Content Provider	IEEE Xplore Digital Library
Author	Hernando, J.B. Schurmann, F. Pastor, L.
Copyright Year	2012
Description	Author affiliation: GMRV, Univ. Rey Juan Carlos, Mostoles, Spain (Pastor, L.) \|\| CeSViMa, Univ. Politec. de Madrid, Madrid, Spain (Hernando, J.B.) \|\| Blue Brain Project, Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland (Schurmann, F.)
Abstract	Recent efforts to model neural tissue in great anatomical detail play an important role in the shift towards simulation-based brain research. Visualization of those detailed models is an important part of the scientific workflow, allowing domain scientists to inspect and analyze the models as a virtual tissue. However, these visualizations pose several computational and interface challenges, from issues such as data size and geometrical and visual complexity. Manual or query driven selection of visual attributes such as color, transparency, transfer functions, visibility are important components for the usability of those visualizations, yet this requires the rendering to be as responsive as possible. Given the data complexity (several million triangles) - despite the power of current GPUs - parallel rendering is the only choice to visualize the full circuits at interactive levels. In particular, sort-first is an appropriate choice to deal with transparency, however it requires efficient view frustum culling. This paper presents two novel techniques for view frustum culling based on a bounding volume skeleton which is created from the geometrical skeleton of the neurons. These skeletons are combined with spatial partitions and some GPU processing to extract the portions of the neurons that need to be rendered while still facilitating interactive changes in the attributes. Experimental results show that the proposed method yields better results than classical approaches such as OBB-trees and looks promising for sort-first environments.
Starting Page	25
Ending Page	32
File Size	2826484
Page Count	8
File Format	PDF
ISBN	9781467347297
e-ISBN	9781467347303
DOI	10.1109/BioVis.2012.6378589
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Neurons Octrees Graphics processing units Rendering (computer graphics) View frustum culling Cortical neuron simulation visualization Skeleton Arrays
Content Type	Text
Resource Type	Article

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