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R ecent advances in imaging technology for biomedicine, including high-speed microscopy, automated microscopy, and imaging flow cytometry are poised to have a large impact on clinical diagnostics, drug discovery, and biological research. Enhanced acquisition speed, resolution, and automation of sample handling are enabling researchers to probe biological phenomena at an increasing rate and achieve intuitive image-based results. However, the rich image sets produced by these tools are massive, possessing potentially millions of frames with tremendous depth and complexity. As a result, the tools introduce immense computational requirements, and, more importantly, the fact that image analysis operates at a much lower speed than image acquisition limits its ability to play a role in critical tasks in biomedicine such as real-time decision making. In this work, we present our strategy for high-throughput image analysis on a graphical processing unit platform. We scrutinized our original algorithm for detecting, tracking, and analyzing cell morphology in high-speed images and identified inefficiencies in image filtering and potential shortcut routines in the morphological analysis stage. Using our ''grid method'' for image enhancements resulted in an 8.54Â reduction in total run time, whereas origin centering allowed using a look up table for coordinate transformation, which reduced the total run time by 55.64Â. Optimization of parallelization and implementation of specialized image processing hardware will ultimately enable real-time analysis of high-throughput image streams and bring wider adoption of assays based on new imaging technologies. ( JALA 2011;16:422-30) INTRODUCTIONImaging is ubiquitous in industrial processing, medicine, environmental science, and cell biology. Given the diverse modes of imaging that exist, an image can contain a wealth of information about an object. Process quality control in semiconductor manufacturing and particle synthesis uses a number of spatial metrics from images from scanning electron microscopy, transmission electron microscopy, atomic force microscopy, and optical microscopy. 1,2 Imaging tools including positron emission tomography, X-ray, magnetic resonance imaging, and computed tomography are widely used in medicine for diagnostic and prognostic purposes. Ocean and waterway monitoring, a critical charge of environmental science, can be performed with high-speed camera-coupled flow cytometry whereby the diversity and density of microscopic organisms, key indicators of ecosystem health, can be identified. 3,4 In cell biology, for example, cell size, morphology, and location can be extracted from brightfield or phase-contrast images. And, the presence or location of biomolecules within cells can be obtained from fluorescence images of chemically labeled cells, which has recently been implemented with automated fluid handling and imagers for high-content analysis.5 As technology improves, imaging rates and resolution increase and the cost of acquiring image sets decreases ...

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Imaging flow cytometry uses high-speed flows and a camera to capture morphological features of hundreds to thousands of cells per second. These morphological features can be useful to isolate sub-populations of cells for life science research and diagnostics. Our experimental setup utilizes a high speed 208×32 resolution CMOS camera, operating at over 140,000 frames per second (FPS). In each frame, the analysis routine detects the presence of an object, and performs morphology measurements. Real-time cell sorting requires a latency under 10 ms in addition to a throughput of 140,000 FPS. In this paper, we will describe GPU and FPGA accelerated implementations of the image analysis necessary for an automated cell sorting system. Our FPGA design results in a 38× speedup over software, providing 2,262 FPS with 11.9 ms of latency. Our GPU implementation shows a 22× speedup, supporting 1,318 FPS with 152 ms of latency.