Title:	Monte Carlo Based Real-Time Shape Analysis in Volumes
Authors:	Gurijala, Krishna Wang, Lei Kaufman, Arie
Citation:	WSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 117-126.
Issue Date:	2023
Publisher:	Václav Skala - UNION Agency
Document type:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/54417
ISBN:	978-80-86943-32-9
ISSN:	2464–4617 (print) 2464–4625 (CD/DVD)
Keywords:	difuze;tvarová analýza;Monte Carlo;detekce rakoviny tlustého střeva;funkce přenosu
Keywords in different language:	Shapeton diffusion;shape analysis;Monte Carlo;colon cancer detection;transfer-function
Abstract in different language:	We introduce a Monte Carlo based real-time diffusion process for shape-based analysis in volumetric data. The diffusion process is carried out by using tiny massless particles termed shapetons, which are used to capture the shape information. Initially, these shapetons are randomly distributed inside the voxels of the volume data. The shapetons are then diffused in a Monte Carlo fashion to obtain the shape information. The direction of propagation for the shapetons is monitored by the Volume Gradient Operator (VGO). This operator is known for successfully capturing the shape information and thus the shape information is well captured by the shapeton diffusion method. All the shapetons are diffused simultaneously and all the results can be monitored in real-time. We demonstrate several important applications of our approach including colon cancer detection and design of shape-based transfer functions. We also present supporting results for the applications and show that this method works well for volumes. We show that our approach can robustly extract shape-based features and thus forms the basis for improved classification and exploration of features based on shape.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2023: Full Papers Proceedings

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