On 1st February 1, 2010, Won-Ki Jeong, Research Scientist at Harvard IIC/SEAS, gave a SciGPU lunchtime seminar entitled:
“GPU-accelerated Biomedical Image Analysis”
Download his presentation here [20MB].
Abstract
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Determining the detailed connections in brain circuits is a fundamental unsolved problem in neuroscience. Understanding this circuitry will enable brain scientists to confirm or refute existing models, develop new ones, and come closer to an understanding of how the brain works. High-resolution, large-scale medical images play a central role in brain analysis and also pose challenging computational problems for 3D segmentation and visualization in terms of developing suitable algorithms, coping with ever-increasing data sizes, and maintaining interactive performance.
In this talk, I will introduce my past and recent research results in GPU-accelerated biomedical image analysis. Specifically, I will talk about the Fast Iterative Method, a parallel algorithm to solve a class of Hamilton-Jacobi equations for weighted distance computation and its application in DT-MRI white matter connectivity analysis. Second, I will introduce NeuroTrace, a GPU-accelerated semi-automatic segmentation and interactive visualization system for processing terabytes of electron microscopy image data, a first step toward the complete reconstruction of neural connections in the mammalian brain.
About the speaker
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Won-Ki Jeong is a research scientist at the Initiative in Innovative Computing (IIC) in the School of Engineering and Applied Science (SEAS) at Harvard University. His research interests include image processing, scientific visualization, and general purpose computing on the graphics processor in the field of biomedical image analysis. He received a Ph.D. in Computer Science in 2008 from the University of Utah, where he was a member of the Scientific Computing and Imaging (SCI) Institute. He received an NVIDIA Fellowship in 2007. He is currently a professional member of ACM.
