Accelerating phase unwrapping and affine transformations for optical quadrature microscopy using CUDA
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, U.S.A
In GPGPU-2: Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units (2009), pp. 28-37
@conference{mistry2009accelerating,
title={Accelerating phase unwrapping and affine transformations for optical quadrature microscopy using CUDA},
author={Mistry, P. and Braganza, S. and Kaeli, D. and Leeser, M.},
booktitle={Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units},
pages={28–37},
year={2009},
organization={ACM}
}
Optical Quadrature Microscopy (OQM) is a process which uses phase data to capture information about the sample being studied. OQM is part of an imaging framework developed by the Optical Science Laboratory at Northeastern University. In one particular application of interest, the framework is used to extract phase information from the image of an embryo to determine embryo viability. Phase Unwrapping is the process of reconstructing the real phase shift (propagation delay) of a sample from the measured “wrapped” representation which is between -pi and +pi. Unwrapping can be done using the Minimum L^P Norm Phase Unwrap algorithm. Images are first preprocessed using an Affine Transform before they are unwrapped. Both of these steps are time consuming and would benefit greatly from parallelization and acceleration. Faster processing would lower many research barriers (in terms of throughput and performance) present when using OQM. In this paper we report on accelerating Phase Unwrapping and Affine Transformations using NVIDIA’s CUDA programming model. We also run elementary noise removal on the GPU using NVIDIA’s CUBLAS (CUDA Basic Linear Algebra Subprograms) library. We integrate GPU execution into a Matlab environment to seamlessly interface to the pre-existing image acquisition system. By mapping the unwrap and noise removal to a GPU, and by also reducing the amount of I/O overhead, we are able to accelerate the end-to-end process by more than 7.3x. This enables our imaging framework to perform high speed image acquisition and visualization at near real-time rates.
January 6, 2011 by hgpu
