Automatic visualizaion pipeline formation for medical on grid computing environment

Distance visualization of large datasets often takes the direction of remote viewing and zooming techniques of stored static images. However, the continuous increase in the size of datasets and visualization operation causes insufficient performance with traditional desktop computers. Additionally,...

Full description

Saved in:
Bibliographic Details
Main Authors: Ahmed, Aboamama Atahar, Abd. Latif, Muhammad Shafie, Abu Bakar, Kamalrulnizam, Ahmad Rajion, Zainul
Format: Article
Language:English
Published: 2008
Subjects:
Online Access:http://eprints.utm.my/id/eprint/18818/1/Aboamama%20Atahar%20Ahmed2008_Automatic-Visualization-Pipeline-Formation-for-Medical-Datasets-on-Grid-Computing-Environment.pdf
http://eprints.utm.my/id/eprint/18818/
https://www.researchgate.net/publication/49910732_Automatic_visualization_pipeline_formation_for_medical_datasets_on_grid_computing_environment
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Distance visualization of large datasets often takes the direction of remote viewing and zooming techniques of stored static images. However, the continuous increase in the size of datasets and visualization operation causes insufficient performance with traditional desktop computers. Additionally, the visualization techniques such as Isosurface depend on the available resources of the running machine and the size of datasets. Moreover, the continuous demand for powerful computing powers and continuous increase in the size of datasets results an urgent need for a grid computing infrastructure. However, some issues arise in current grid such as resources availability at the client machines which are not sufficient enough to process large datasets. On top of that, different output devices and different network bandwidth between the visualization pipeline components often result output suitable for one machine and not suitable for another. In this paper we investigate how the grid services could be used to support remote visualization of large datasets and to break the constraint of physical co-location of the resources by applying the grid computing technologies. We show our grid enabled architecture to visualize large medical datasets (circa 5 million polygons) for remote interactive visualization on modest resources clients