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GPU accelerated vessel segmentation using Laplacian eigenmaps

with Lin Cheng, Peter Yoon and Jiajia Zhao

Paper presented at IASTED PDCN (2014)
Poster presented at the GPU Technology Conference (2013)

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• Code: https://github.com/hcho3/eigenmap_gpu
• GTC poster: [PDF]
• IASTED PDCN talk: [PPTX] [PDF]
• IASTED PDCN paper: [Abstract] [Paper]

Synopsis

Laplacian eigenmap is an image segmentation algorithm that began to gain traction in recent years. It involves a generalized eigenvalue problem which extracts high-level features from local neighborhood information. Unfortunately, it is computationally costly to compute eigenvalues of a large linear systems. We make use of general-purpose GPUs to accelerate the segmentation process.

Publication Details

• Conference Paper:
  Lin Cheng, Hyunsu Cho, and Peter Yoon. “GPU Accelerated Vessel Segmentation Using Laplacian Eigenmaps,” Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks, pp. 177-184, Innsbruck, Austria, February 17, 2014.
• Poster:
  Lin Cheng, Hyunsu Cho, Peter Yoon, and Jiajia Zhao. “GPU Accelerated Vessel Segmentation Using Laplacian Eigenmaps,” The GPU Technology Conference 2013, San Jose, CA, March 18, 2013.

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