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Jeong, Won-Ki
High-performance Visual Computing Lab
Research Interests
  • Visualization, image processing, machine learning, parallel computing

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Markov surfaces: A probabilistic framework for user-assisted three-dimensional image segmentation

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Title
Markov surfaces: A probabilistic framework for user-assisted three-dimensional image segmentation
Author
Pan, YongshengJeong, Won-KiWhitaker, Ross
Issue Date
2011-10
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Citation
COMPUTER VISION AND IMAGE UNDERSTANDING, v.115, no.10, pp.1375 - 1383
Abstract
This paper presents Markov surfaces, a probabilistic algorithm for user-assisted segmentation of elongated structures in 3D images. The 3D segmentation problem is formulated as a path-finding problem, where path probabilities are described by Markov chains. Users define points, curves, or regions on 2D image slices, and the algorithm connects these user-defined features in a way that respects the underlying elongated structure in data. Transition probabilities in the Markov model are derived from intensity matches and interslice correspondences, which are generated from a slice-to-slice registration algorithm. Bezier interpolations between paths are applied to generate smooth surfaces. Subgrid accuracy is achieved by linear interpolations of image intensities and the interslice correspondences. Experimental results on synthetic and real data demonstrate that Markov surfaces can segment regions that are defined by texture, nearby context, and motion. A parallel implementation on a streaming parallel computer architecture, a graphics processor, makes the method interactive for 3D data.
URI
https://scholarworks.unist.ac.kr/handle/201301/7965
URL
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=79959919338
DOI
10.1016/j.cviu.2011.06.003
ISSN
1077-3142
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