Acceleration of direct volume rendering with programmable graphics hardware

Keleş, Hacer Yalım; Es, A.; İşler, Veysi

doi:10.1007/s00371-006-0084-5

Cited by 7 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Programming a GPU using shading languages was still based on graphics pipeline, yet it was easier to utilize the computational power of GPUs for general purpose programming. This attracted many researchers in the field to optimize computationally expensive algorithms by redesigning the data structures and the data flow for these GPU architectures (Keles et al, 2006;Rudomin et al, 2005;Ruiz et al, 2008). CUDA provides a programming model which enables utilization of massive data parallelism through the abstractions for thread groups, shared memory and thread synchronization with a minimal set of extensions to C programming language (Owens et al, 2008).…”

Section: Related Workmentioning

confidence: 99%

Acceleration of the edge strength function on GPU using CUDA

Keleş¹,

Hacer²

2016

Communications Faculty of Science University of Ankara

View full text Add to dashboard Cite

Edge strength function (ESF) generates a family of level curves that evolves under the influence of curvature motions. This function is proved to be useful in representing images in computer vision for analysis and recognition purposes in different contexts. Computation of the ESF requires solving a partial differential equation, hence computationally costly. In this work, we present two parallel implementations of ESF that work on Graphics Processing Units (GPU) using Compute Unified Design Architecture (CUDA). Both implementations reduce the computational time significantly with respect to their serial counterpart. The implementations differ mainly in the type of memory that is utilized for accessing data; the first approach utilizes the shared memory and the second one utilizes the texture memory. We obtain between 40 to 65 times speedup in the shared memory based implementation and between 35 to 55 times in the texture memory based implementation with respect to the single threaded CPU implementation. The amount of speedup changes depending on the data size..

show abstract

Section: Related Workmentioning

confidence: 99%

Acceleration of the edge strength function on GPU using CUDA

Keleş¹,

Hacer²

2016

Communications Faculty of Science University of Ankara

View full text Add to dashboard Cite

show abstract

Medical Image Volumetric Visualization: Algorithms, Pipelines, and Surgical Applications

Zhang

Peters

Eagleson

2011

Medical Image Processing

View full text Add to dashboard Cite

Volume Visualization: A Technical Overview with a Focus on Medical Applications

2010

View full text Add to dashboard Cite

With the increasing availability of high-resolution isotropic three-or four-dimensional medical datasets from sources such as magnetic resonance imaging, computed tomography, and ultrasound, volumetric image visualization techniques have increased in importance. Over the past two decades, a number of new algorithms and improvements have been developed for practical clinical image display. More recently, further efficiencies have been attained by designing and implementing volumerendering algorithms on graphics processing units (GPUs). In this paper, we review volumetric image visualization pipelines, algorithms, and medical applications. We also illustrate our algorithm implementation and evaluation results, and address the advantages and drawbacks of each algorithm in terms of image quality and efficiency. Within the outlined literature review, we have integrated our research results relating to new visualization, classification, enhancement, and multimodal data dynamic rendering. Finally, we illustrate issues related to modern GPU working pipelines, and their applications in volume visualization domain.

show abstract

Acceleration of direct volume rendering with programmable graphics hardware

Cited by 7 publications

References 12 publications

Acceleration of the edge strength function on GPU using CUDA

Acceleration of the edge strength function on GPU using CUDA

Medical Image Volumetric Visualization: Algorithms, Pipelines, and Surgical Applications

Volume Visualization: A Technical Overview with a Focus on Medical Applications

Contact Info

Product

Resources

About