Region-Based Conditional Random Fields For Medical Image Labeling
Available Online August 2015.
- 10.2991/meita-15.2015.35How to use a DOI?
- conditional random fields (CRFs), image labeling, over-segmented,medical image
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.
- © 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/).
Cite this article
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 -