Accelerating phase unwrapping and affine transformations for optical quadrature microscopy using CUDA

Abstract

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 −π and +π. 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

Region-referenced phase unwrapping architecture for digital holographic microscopy

“…In addition, these implementations are realized by different platforms. The proposed architecture and the implementations in [13,14,16] are based on iterative phase-unwrapping algorithms. As compared with these implementations, the proposed architecture has the lowest computation time.…”

“…It is interesting to note that the algorithms implemented by [13,14,16] belong to a family of algorithms using minimum norm techniques [1]. They are pathindependent.…”

“…They are pathindependent. In addition, some techniques are able to provide solutions minimizing the number of phase discontinuities [13,14]. The proposed architecture is based on the region-referenced method.…”

“…The implementations presented in [13,14] are based on PCG [1,5] or minimum L P norm [1] with field programmable gate array (FPGA) [15] devices and graphic processing unit (GPU) platforms, respectively. The implementation proposed in [16] is based on Gauss-Seidel techniques [1,6] with GPU platforms.…”

Medical image processing on the GPU – Past, present and future

“…Orzo et al (2010) presented similar work, but instead reconstructed volume data. Other examples of GPU accelerated microscopy include phase unwrapping and affine transformations for optical quadrature microscopy (Mistry et al, 2009), image analysis for localization-based super resolution microscopy for imaging of live cells (Quan et al, 2010) and reconstruction of volumes in tomographic diffractive microscopy (Bailleul et al, 2012).…”