Neural Filter with Selection of Input Features and Its Application to Image Quality Improvement of Medical Image Sequences
Summary :
In this paper, we propose a new neural filter to which the features related to a given task are input, called a neural filter with features (NFF), to improve further the performance of the conventional neural filter. In order to handle the issue concerning the optimal selection of input features, we propose a framework composed of 1) manual selection of candidates for input features related to a given task and 2) training with automatically selection of the optimal input features required for achieving the given task. Experiments on the proposed framework with an application to improving the image quality of medical X-ray image sequences were performed. The experimental results demonstrated that the performance on edge-preserving smoothing of the NFF, obtained by the proposed framework, is superior to that of the conventional neural and dynamic filters.
- Publication
- IEICE TRANSACTIONS on Information Vol.E85-D No.10 pp.1710-1718
- Publication Date
- 2002/10/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Medical Engineering
Authors
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
Kenji SUZUKI, Isao HORIBA, Noboru SUGIE, Michio NANKI, "Neural Filter with Selection of Input Features and Its Application to Image Quality Improvement of Medical Image Sequences" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 10, pp. 1710-1718, October 2002, doi: .
Abstract: In this paper, we propose a new neural filter to which the features related to a given task are input, called a neural filter with features (NFF), to improve further the performance of the conventional neural filter. In order to handle the issue concerning the optimal selection of input features, we propose a framework composed of 1) manual selection of candidates for input features related to a given task and 2) training with automatically selection of the optimal input features required for achieving the given task. Experiments on the proposed framework with an application to improving the image quality of medical X-ray image sequences were performed. The experimental results demonstrated that the performance on edge-preserving smoothing of the NFF, obtained by the proposed framework, is superior to that of the conventional neural and dynamic filters.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_10_1710/_p
Copy
@ARTICLE{e85-d_10_1710,
author={Kenji SUZUKI, Isao HORIBA, Noboru SUGIE, Michio NANKI, },
journal={IEICE TRANSACTIONS on Information},
title={Neural Filter with Selection of Input Features and Its Application to Image Quality Improvement of Medical Image Sequences},
year={2002},
volume={E85-D},
number={10},
pages={1710-1718},
abstract={In this paper, we propose a new neural filter to which the features related to a given task are input, called a neural filter with features (NFF), to improve further the performance of the conventional neural filter. In order to handle the issue concerning the optimal selection of input features, we propose a framework composed of 1) manual selection of candidates for input features related to a given task and 2) training with automatically selection of the optimal input features required for achieving the given task. Experiments on the proposed framework with an application to improving the image quality of medical X-ray image sequences were performed. The experimental results demonstrated that the performance on edge-preserving smoothing of the NFF, obtained by the proposed framework, is superior to that of the conventional neural and dynamic filters.},
keywords={},
doi={},
ISSN={},
month={October},}
Copy
TY - JOUR
TI - Neural Filter with Selection of Input Features and Its Application to Image Quality Improvement of Medical Image Sequences
T2 - IEICE TRANSACTIONS on Information
SP - 1710
EP - 1718
AU - Kenji SUZUKI
AU - Isao HORIBA
AU - Noboru SUGIE
AU - Michio NANKI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2002
AB - In this paper, we propose a new neural filter to which the features related to a given task are input, called a neural filter with features (NFF), to improve further the performance of the conventional neural filter. In order to handle the issue concerning the optimal selection of input features, we propose a framework composed of 1) manual selection of candidates for input features related to a given task and 2) training with automatically selection of the optimal input features required for achieving the given task. Experiments on the proposed framework with an application to improving the image quality of medical X-ray image sequences were performed. The experimental results demonstrated that the performance on edge-preserving smoothing of the NFF, obtained by the proposed framework, is superior to that of the conventional neural and dynamic filters.
ER -
English
