Parallelized ray casting volume rendering and 3D segmentation with combinatorial map
The rapid development of digital technology has enabled the real-time volume rendering of scientific data, in particular large microscopy data sets. In general, volume rendering techniques project 3D discrete datasets onto 2D image planes, with the generated views being transparent and having designated color that is not necessarily "real" color. Volume rendering techniques initially require designating a processing method that assigns different colors and transparency coefficients to different regions. Then based on the "viewer" and the dataset "location", the method will determine the final imaging effect. Current popular techniques includes ray casting, splatting, shear warp, and texture-based volume rendering. Of particular interest is ray casting as it permits the display of objects interior to a dataset as well as render complex objects such as skeleton and muscle. However, ray casting requires large memory and suffers from longer processing time. One way to address this is to parallelize its implementation on programmable graphic processing hardware. This thesis proposes a GPU based ray casting algorithm that can render a 3D volume in real-time application. In addition, to implementing volume rendering techniques on programmable graphic processing hardware to decrease execution times, 3D image segmentation techniques can also be utilized to increase execution speeds. In 3D image segmentation, the dataset is partitioned into smaller sized regions based on specific properties. By using a 3D segmentation method in volume rendering applications, users can extract individual objects from within the 3D dataset for rendering and further analysis. This thesis proposes a 3D segmentation algorithm with combinatorial map that can be parallelized on graphic processing units.
Salama, Purdue University.
Computer Engineering|Electrical engineering
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