Speeding up Mutual Information Computation Using NVIDIA CUDA Hardware

Authors:
Ramtin Shams;Nick Barnes
Affiliations:
-;-
Venue:
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
Year:
2007

Citing 0
Cited 7

Real-Time Simulation of Medical Ultrasound from CT Images

MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Parallel computation of mutual information on the GPU with application to real-time registration of 3D medical images

Computer Methods and Programs in Biomedicine
Parallelized computation for computer simulation of electrocardiograms using personal computers with multi-core CPU and general-purpose GPU

Computer Methods and Programs in Biomedicine
A survey of medical image registration on graphics hardware

Computer Methods and Programs in Biomedicine
CUDA optimization strategies for compute- and memory-bound neuroimaging algorithms

Computer Methods and Programs in Biomedicine
Isosurface similarity maps

EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Information-theoretic analysis of molecular (co)evolution using graphics processing units

Proceedings of the 3rd international workshop on Emerging computational methods for the life sciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an efficient method for mutual information (MI) computation between images (2D or 3D) for NVIDIA's `compute unified device architecture' (CUDA) compatible devices. Efficient parallelization of MI is particularly challenging on a `graphics processor unit' (GPU) due to the need for histogram-based calculation of joint and marginal probability mass functions (pmfs) with large number of bins. The data-dependent (unpredictable) nature of the updates to the histogram, together with hardware limitations of the GPU (lack of synchronization primitives and limited memory caching mechanisms) can make GPU-based computation inefficient. To overcome these limitation, we approximate the pmfs, using a down-sampled version of the joint- histogram which avoids memory update problems. Our CUDA implementation improves the efficiency of MI calculations by a factor of 25 compared to a standard CPU- based implementation and can be used in MI-based image registration applications.