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Assembling large mosaics of electron microscope images using GPU

Kannan Umadevi Venkataraju, Mark Kim, Dan Gerszewski, James R. Anderson, Mary Hall
Scientific Computing and Imaging Institute, University of Utah
Symposium on Application Accelerators in High Performance Computing, 2009 (SAAHPC’09)

@article{venkataraju2009assembling,

   title={Assembling large mosaics of electron microscope images using GPU},

   author={Venkataraju, K.U. and Kim, M. and Gerszewski, D. and Anderson, J.R. and Hall, M.},

   booktitle={Application Accelerators in High Performance Computing, 2009 Symposium, Papers},

   year={2009}

}

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Understanding the neural circuitry of the retina requires us to map the connectivity of individual neurons in large neuronal tissue sections and analyze signal communication across processes from the electron microscopy images. One of the major bottlenecks in the critical path is the image mosaicing process where 2D slices are assembled from scanned microscopy image tiles. The problem of assembling the tiles is computationally non-trivial because of distortion of the specimen in the electron microscope due to heat and overlap between the scanned tiles. The complexity of the calculation arises from the massive size of the dataset and mathematical calculations required to calculate value of each pixel of the mosaic. We propose to use texture memory lookups to speedup the access to image tiles and data parallel computing enabled by the GPUs to accelerate this process. The proposed method results in noticeable improvements in speed of computation compared to other methods.
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