IEICE TRANSACTIONS on Information
A Model Optimization Approach to the Automatic Segmentation of Medical Images
Summary :
The aim of this work is to develop an efficient medical image segmentation technique by fitting a nonlinear shape model with pre-segmented images. In this technique, the kernel principle component analysis (KPCA) is used to capture the shape variations and to build the nonlinear shape model. The pre-segmentation is carried out by classifying the image pixels according to the high level texture features extracted using the over-complete wavelet packet decomposition. Additionally, the model fitting is completed using the particle swarm optimization technique (PSO) to adapt the model parameters. The proposed technique is fully automated, is talented to deal with complex shape variations, can efficiently optimize the model to fit the new cases, and is robust to noise and occlusion. In this paper, we demonstrate the proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans and the obtained results are very hopeful.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.4 pp.882-890
- Publication Date
- 2010/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.882
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
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Keyword
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Ahmed AFIFI, Toshiya NAKAGUCHI, Norimichi TSUMURA, Yoichi MIYAKE, "A Model Optimization Approach to the Automatic Segmentation of Medical Images" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 4, pp. 882-890, April 2010, doi: 10.1587/transinf.E93.D.882.
Abstract: The aim of this work is to develop an efficient medical image segmentation technique by fitting a nonlinear shape model with pre-segmented images. In this technique, the kernel principle component analysis (KPCA) is used to capture the shape variations and to build the nonlinear shape model. The pre-segmentation is carried out by classifying the image pixels according to the high level texture features extracted using the over-complete wavelet packet decomposition. Additionally, the model fitting is completed using the particle swarm optimization technique (PSO) to adapt the model parameters. The proposed technique is fully automated, is talented to deal with complex shape variations, can efficiently optimize the model to fit the new cases, and is robust to noise and occlusion. In this paper, we demonstrate the proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans and the obtained results are very hopeful.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.882/_p
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@ARTICLE{e93-d_4_882,
author={Ahmed AFIFI, Toshiya NAKAGUCHI, Norimichi TSUMURA, Yoichi MIYAKE, },
journal={IEICE TRANSACTIONS on Information},
title={A Model Optimization Approach to the Automatic Segmentation of Medical Images},
year={2010},
volume={E93-D},
number={4},
pages={882-890},
abstract={The aim of this work is to develop an efficient medical image segmentation technique by fitting a nonlinear shape model with pre-segmented images. In this technique, the kernel principle component analysis (KPCA) is used to capture the shape variations and to build the nonlinear shape model. The pre-segmentation is carried out by classifying the image pixels according to the high level texture features extracted using the over-complete wavelet packet decomposition. Additionally, the model fitting is completed using the particle swarm optimization technique (PSO) to adapt the model parameters. The proposed technique is fully automated, is talented to deal with complex shape variations, can efficiently optimize the model to fit the new cases, and is robust to noise and occlusion. In this paper, we demonstrate the proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans and the obtained results are very hopeful.},
keywords={},
doi={10.1587/transinf.E93.D.882},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - A Model Optimization Approach to the Automatic Segmentation of Medical Images
T2 - IEICE TRANSACTIONS on Information
SP - 882
EP - 890
AU - Ahmed AFIFI
AU - Toshiya NAKAGUCHI
AU - Norimichi TSUMURA
AU - Yoichi MIYAKE
PY - 2010
DO - 10.1587/transinf.E93.D.882
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2010
AB - The aim of this work is to develop an efficient medical image segmentation technique by fitting a nonlinear shape model with pre-segmented images. In this technique, the kernel principle component analysis (KPCA) is used to capture the shape variations and to build the nonlinear shape model. The pre-segmentation is carried out by classifying the image pixels according to the high level texture features extracted using the over-complete wavelet packet decomposition. Additionally, the model fitting is completed using the particle swarm optimization technique (PSO) to adapt the model parameters. The proposed technique is fully automated, is talented to deal with complex shape variations, can efficiently optimize the model to fit the new cases, and is robust to noise and occlusion. In this paper, we demonstrate the proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans and the obtained results are very hopeful.
ER -
English
