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Supervised Manifold Distance Segmentation
November 2011 (vol. 17 no. 11)
pp. 1637-1649
ASCII Text
x 
 
Joe Kniss, Guanyu Wang, "Supervised Manifold Distance Segmentation," IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 11, pp. 1637-1649, November, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2010.120,
author = {Joe Kniss and Guanyu Wang},
title = {Supervised Manifold Distance Segmentation},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {17},
number = {11},
issn = {1077-2626},
year = {2011},
pages = {1637-1649},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.120},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Supervised Manifold Distance Segmentation
IS - 11
SN - 1077-2626
SP1637
EP1649
EPD - 1637-1649
A1 - Joe Kniss,
A1 - Guanyu Wang,
PY - 2011
KW - Hypothesis testing
KW - visual evidence
KW - data segmentation
KW - extraction of surfaces (isosurfaces
KW - material boundaries)
KW - multifield
KW - multimodal
KW - and multivariate data
KW - uncertainty visualization.
VL - 17
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Joe Kniss, University of New Mexico, Albuquerque
Guanyu Wang, University of New Mexico, Albuquerque
We present a simple and robust method for image and volume data segmentation based on manifold distance metrics. This is done by treating the image as a function that maps the 2D (image) or 3D (volume) to a 2D or 3D manifold in a higher dimensional feature space. We explore a range of possible feature spaces, including value, gradient, and probabilistic measures, and examine the consequences of including these measures in the feature space. The time and space computational complexity of our segmentation algorithm is O(N), which allows interactive, user-centric segmentation even for large data sets. We show that this method, given appropriate choice of feature vector, produces results both qualitatively and quantitatively similar to Level Sets, Random Walkers, and others. We validate the robustness of this segmentation scheme with comparisons to standard ground-truth models and sensitivity analysis of the algorithm.

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Index Terms:
Hypothesis testing, visual evidence, data segmentation, extraction of surfaces (isosurfaces, material boundaries), multifield, multimodal, and multivariate data, uncertainty visualization.
Citation:
Joe Kniss, Guanyu Wang, "Supervised Manifold Distance Segmentation," IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 11, pp. 1637-1649, Nov. 2011, doi:10.1109/TVCG.2010.120
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