IEICE TRANSACTIONS on Information
Multiple k-Nearest Neighbor Classifier and Its Application to Tissue Characterization of Coronary Plaque
Summary :
In this paper we propose a novel classification method for the multiple k-nearest neighbor (MkNN) classifier and show its practical application to medical image processing. The proposed method performs fine classification when a pair of the spatial coordinate of the observation data in the observation space and its corresponding feature vector in the feature space is provided. The proposed MkNN classifier uses the continuity of the distribution of features of the same class not only in the feature space but also in the observation space. In order to validate the performance of the present method, it is applied to the tissue characterization problem of coronary plaque. The quantitative and qualitative validity of the proposed MkNN classifier have been confirmed by actual experiments.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.7 pp.1920-1927
- Publication Date
- 2016/07/01
- Publicized
- 2016/04/15
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015EDP7351
- Type of Manuscript
- PAPER
- Category
- Biological Engineering
Authors
Eiji UCHINO
Yamaguchi University,Fuzzy Logic Systems Institute (FLSI)
Ryosuke KUBOTA
Ube College
Takanori KOGA
Tokuyama College
Hideaki MISAWA
Ube College
Noriaki SUETAKE
Yamaguchi University
Keyword
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Eiji UCHINO, Ryosuke KUBOTA, Takanori KOGA, Hideaki MISAWA, Noriaki SUETAKE, "Multiple k-Nearest Neighbor Classifier and Its Application to Tissue Characterization of Coronary Plaque" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 7, pp. 1920-1927, July 2016, doi: 10.1587/transinf.2015EDP7351.
Abstract: In this paper we propose a novel classification method for the multiple k-nearest neighbor (MkNN) classifier and show its practical application to medical image processing. The proposed method performs fine classification when a pair of the spatial coordinate of the observation data in the observation space and its corresponding feature vector in the feature space is provided. The proposed MkNN classifier uses the continuity of the distribution of features of the same class not only in the feature space but also in the observation space. In order to validate the performance of the present method, it is applied to the tissue characterization problem of coronary plaque. The quantitative and qualitative validity of the proposed MkNN classifier have been confirmed by actual experiments.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDP7351/_p
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@ARTICLE{e99-d_7_1920,
author={Eiji UCHINO, Ryosuke KUBOTA, Takanori KOGA, Hideaki MISAWA, Noriaki SUETAKE, },
journal={IEICE TRANSACTIONS on Information},
title={Multiple k-Nearest Neighbor Classifier and Its Application to Tissue Characterization of Coronary Plaque},
year={2016},
volume={E99-D},
number={7},
pages={1920-1927},
abstract={In this paper we propose a novel classification method for the multiple k-nearest neighbor (MkNN) classifier and show its practical application to medical image processing. The proposed method performs fine classification when a pair of the spatial coordinate of the observation data in the observation space and its corresponding feature vector in the feature space is provided. The proposed MkNN classifier uses the continuity of the distribution of features of the same class not only in the feature space but also in the observation space. In order to validate the performance of the present method, it is applied to the tissue characterization problem of coronary plaque. The quantitative and qualitative validity of the proposed MkNN classifier have been confirmed by actual experiments.},
keywords={},
doi={10.1587/transinf.2015EDP7351},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Multiple k-Nearest Neighbor Classifier and Its Application to Tissue Characterization of Coronary Plaque
T2 - IEICE TRANSACTIONS on Information
SP - 1920
EP - 1927
AU - Eiji UCHINO
AU - Ryosuke KUBOTA
AU - Takanori KOGA
AU - Hideaki MISAWA
AU - Noriaki SUETAKE
PY - 2016
DO - 10.1587/transinf.2015EDP7351
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2016
AB - In this paper we propose a novel classification method for the multiple k-nearest neighbor (MkNN) classifier and show its practical application to medical image processing. The proposed method performs fine classification when a pair of the spatial coordinate of the observation data in the observation space and its corresponding feature vector in the feature space is provided. The proposed MkNN classifier uses the continuity of the distribution of features of the same class not only in the feature space but also in the observation space. In order to validate the performance of the present method, it is applied to the tissue characterization problem of coronary plaque. The quantitative and qualitative validity of the proposed MkNN classifier have been confirmed by actual experiments.
ER -
English
