Statistical Edge Detection in CT Image by Kernel Density Estimation and Mean Square Error Distance
Summary :
In this paper, we develop a novel two-sample test statistic for edge detection in CT image. This test statistic involves the non-parametric estimate of the samples' probability density functions (PDF's) based on the kernel density estimator and the calculation of the mean square error (MSE) distance of the estimated PDF's. In order to extract single-pixel-wide edges, a generic detection scheme cooperated with the non-maximum suppression is also proposed. This new method is applied to a variety of noisy images, and the performance is quantitatively evaluated with edge strength images. The experiments show that the proposed method provides a more effective and robust way of detecting edges in CT image compared with other existing methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.5 pp.1162-1170
- Publication Date
- 2013/05/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.1162
- Type of Manuscript
- PAPER
- Category
- Image Processing and Video Processing
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Keyword
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Xu XU, Yi CUI, Shuxu GUO, "Statistical Edge Detection in CT Image by Kernel Density Estimation and Mean Square Error Distance" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 5, pp. 1162-1170, May 2013, doi: 10.1587/transinf.E96.D.1162.
Abstract: In this paper, we develop a novel two-sample test statistic for edge detection in CT image. This test statistic involves the non-parametric estimate of the samples' probability density functions (PDF's) based on the kernel density estimator and the calculation of the mean square error (MSE) distance of the estimated PDF's. In order to extract single-pixel-wide edges, a generic detection scheme cooperated with the non-maximum suppression is also proposed. This new method is applied to a variety of noisy images, and the performance is quantitatively evaluated with edge strength images. The experiments show that the proposed method provides a more effective and robust way of detecting edges in CT image compared with other existing methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.1162/_p
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@ARTICLE{e96-d_5_1162,
author={Xu XU, Yi CUI, Shuxu GUO, },
journal={IEICE TRANSACTIONS on Information},
title={Statistical Edge Detection in CT Image by Kernel Density Estimation and Mean Square Error Distance},
year={2013},
volume={E96-D},
number={5},
pages={1162-1170},
abstract={In this paper, we develop a novel two-sample test statistic for edge detection in CT image. This test statistic involves the non-parametric estimate of the samples' probability density functions (PDF's) based on the kernel density estimator and the calculation of the mean square error (MSE) distance of the estimated PDF's. In order to extract single-pixel-wide edges, a generic detection scheme cooperated with the non-maximum suppression is also proposed. This new method is applied to a variety of noisy images, and the performance is quantitatively evaluated with edge strength images. The experiments show that the proposed method provides a more effective and robust way of detecting edges in CT image compared with other existing methods.},
keywords={},
doi={10.1587/transinf.E96.D.1162},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - Statistical Edge Detection in CT Image by Kernel Density Estimation and Mean Square Error Distance
T2 - IEICE TRANSACTIONS on Information
SP - 1162
EP - 1170
AU - Xu XU
AU - Yi CUI
AU - Shuxu GUO
PY - 2013
DO - 10.1587/transinf.E96.D.1162
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2013
AB - In this paper, we develop a novel two-sample test statistic for edge detection in CT image. This test statistic involves the non-parametric estimate of the samples' probability density functions (PDF's) based on the kernel density estimator and the calculation of the mean square error (MSE) distance of the estimated PDF's. In order to extract single-pixel-wide edges, a generic detection scheme cooperated with the non-maximum suppression is also proposed. This new method is applied to a variety of noisy images, and the performance is quantitatively evaluated with edge strength images. The experiments show that the proposed method provides a more effective and robust way of detecting edges in CT image compared with other existing methods.
ER -
English
