Region-Based Conditional Random Fields For Medical Image Labeling

DOI

10.2991/meita-15.2015.35How to use a DOI?

Keywords

conditional random fields (CRFs), image labeling, over-segmented,medical image

Abstract

Concerning the high time complexity of medical image labeling in graph model, we proposed a region-based CRF method for medical image labeling. This method first over segmented the image into small homogeneous regions by using over-segmented method, and then the graphical model was constructed with regions as nodes and connecting the neighboring nodes as edges. The corresponding definition of region-based CRF were proposed and implemented. The experimental results shows that better medical image labeling results are obtained by the region-based CRF model. At the same time, running time is largely reduced, efficiency is improved.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yan Yang
PY  - 2015/08
DA  - 2015/08
TI  - Region-Based Conditional Random Fields For Medical Image Labeling
BT  - Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications
PB  - Atlantis Press
SP  - 181
EP  - 185
SN  - 2352-5401
UR  - https://doi.org/10.2991/meita-15.2015.35
DO  - 10.2991/meita-15.2015.35
ID  - Yang2015/08
ER  -