Implementation of Edge-Enhancement Nonlinear Anisotropic Diffusion Filtering Using Different CUDA Memory Models

被引：0
|
作者：
Attia, M. H. [1 ]
Elshehaby, S. A. [1 ]
Elmaghraby, A. S. [2 ]
机构：
[1] Univ Alexandria, Med Res Inst, Dept Biomed Engn, Alexandria, Egypt
[2] Univ Louisville, Comp Engn & Comp Sci Dept, Louisville, KY 40292 USA
来源：
2015 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT) | 2015年
关键词：
CUDA; GPGPU; Shared Memory; Texture Memory; Anisotropic Diffusion; Mammogram;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Graphics Processing Units (GPUs) are used today as affordable energy-efficient method of acceleration for computationally exhaustive algorithms to decrease execution time exploiting the power of parallel programing techniques. In the field of medical imaging, GPUs became crucial acceleration method for computationally exhaustive algorithms. This paper presented the effect of memory optimization on the performance of CUDA accelerated medical image processing algorithms. Edge-enhancement nonlinear anisotropic diffusion is used as a case study to show the effect of utilization of different memory types on the performance.
引用
收藏
页码：501 / 504
页数：4
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