The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
O objetivo deste trabalho é desenvolver uma técnica eficiente de segmentação de imagens médicas, ajustando um modelo de forma não linear com imagens pré-segmentadas. Nesta técnica, a análise de componentes do princípio do kernel (KPCA) é usada para capturar as variações de forma e construir o modelo de forma não linear. A pré-segmentação é realizada classificando os pixels da imagem de acordo com as características de textura de alto nível extraídas usando a decomposição completa de pacotes wavelet. Além disso, o ajuste do modelo é concluído usando a técnica de otimização por enxame de partículas (PSO) para adaptar os parâmetros do modelo. A técnica proposta é totalmente automatizada, é talentosa para lidar com variações complexas de forma, pode otimizar eficientemente o modelo para se adequar aos novos casos e é robusta a ruído e oclusão. Neste artigo, demonstramos a técnica proposta implementando-a na segmentação do fígado a partir de tomografia computadorizada (TC) e os resultados obtidos são muito esperançosos.
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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},}
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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 -