Interleaved k-NN Classification and Bias Field Estimation for MR Image with Intensity Inhomogeneity
Summary :
k-NN classification has been applied to classify normal tissues in MR images. However, the intensity inhomogeneity of MR images forces conventional k-NN classification into significant misclassification errors. This letter proposes a new interleaved method, which combines k-NN classification and bias field estimation in an energy minimization framework, to simultaneously overcome the limitation of misclassifications in conventional k-NN classification and correct the bias field of observed images. Experiments demonstrate the effectiveness and advantages of the proposed algorithm.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.4 pp.1011-1015
- Publication Date
- 2014/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E97.D.1011
- Type of Manuscript
- LETTER
- Category
- Biological Engineering
Authors
Jingjing GAO
University of Electronic Science and Technology of China
Mei XIE
University of Electronic Science and Technology of China
Ling MAO
University of Electronic Science and Technology of China
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Jingjing GAO, Mei XIE, Ling MAO, "Interleaved k-NN Classification and Bias Field Estimation for MR Image with Intensity Inhomogeneity" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 4, pp. 1011-1015, April 2014, doi: 10.1587/transinf.E97.D.1011.
Abstract: k-NN classification has been applied to classify normal tissues in MR images. However, the intensity inhomogeneity of MR images forces conventional k-NN classification into significant misclassification errors. This letter proposes a new interleaved method, which combines k-NN classification and bias field estimation in an energy minimization framework, to simultaneously overcome the limitation of misclassifications in conventional k-NN classification and correct the bias field of observed images. Experiments demonstrate the effectiveness and advantages of the proposed algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.1011/_p
Copy
@ARTICLE{e97-d_4_1011,
author={Jingjing GAO, Mei XIE, Ling MAO, },
journal={IEICE TRANSACTIONS on Information},
title={Interleaved k-NN Classification and Bias Field Estimation for MR Image with Intensity Inhomogeneity},
year={2014},
volume={E97-D},
number={4},
pages={1011-1015},
abstract={k-NN classification has been applied to classify normal tissues in MR images. However, the intensity inhomogeneity of MR images forces conventional k-NN classification into significant misclassification errors. This letter proposes a new interleaved method, which combines k-NN classification and bias field estimation in an energy minimization framework, to simultaneously overcome the limitation of misclassifications in conventional k-NN classification and correct the bias field of observed images. Experiments demonstrate the effectiveness and advantages of the proposed algorithm.},
keywords={},
doi={10.1587/transinf.E97.D.1011},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Interleaved k-NN Classification and Bias Field Estimation for MR Image with Intensity Inhomogeneity
T2 - IEICE TRANSACTIONS on Information
SP - 1011
EP - 1015
AU - Jingjing GAO
AU - Mei XIE
AU - Ling MAO
PY - 2014
DO - 10.1587/transinf.E97.D.1011
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2014
AB - k-NN classification has been applied to classify normal tissues in MR images. However, the intensity inhomogeneity of MR images forces conventional k-NN classification into significant misclassification errors. This letter proposes a new interleaved method, which combines k-NN classification and bias field estimation in an energy minimization framework, to simultaneously overcome the limitation of misclassifications in conventional k-NN classification and correct the bias field of observed images. Experiments demonstrate the effectiveness and advantages of the proposed algorithm.
ER -
English
