A Deformable Surface Model Based on Boundary and Region Information for Pulmonary Nodule Segmentation from 3-D Thoracic CT Images
Summary :
Accurately segmenting and quantifying pulmonary nodule structure is a key issue in three-dimensional (3-D) computer-aided diagnosis (CAD) schemes. This paper presents a nodule segmentation method from 3-D thoracic CT images based on a deformable surface model. In this method, first, a statistical analysis of the observed intensity is performed to measure differences between the nodule and other regions. Based on this analysis, the boundary and region information are represented by boundary and region likelihood, respectively. Second, an initial surface in the nodule is manually set. Finally, the deformable surface model moves the initial surface so that the surface provides high boundary likelihood and high posterior segmentation probability with respect to the nodule. For the purpose, the deformable surface model integrates the boundary and region information. This integration makes it possible to cope with inappropriate position or size of an initial surface in the nodule. Using the practical 3-D thoracic CT images, we demonstrate the effectiveness of the proposed method.
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- IEICE TRANSACTIONS on Information Vol.E86-D No.9 pp.1921-1930
- Publication Date
- 2003/09/01
- Publicized
- Online ISSN
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- Type of Manuscript
- PAPER
- Category
- Medical Engineering
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Yoshiki KAWATA, Noboru NIKI, Hironobu OHMATSU, Noriyuki MORIYAMA, "A Deformable Surface Model Based on Boundary and Region Information for Pulmonary Nodule Segmentation from 3-D Thoracic CT Images" in IEICE TRANSACTIONS on Information,
vol. E86-D, no. 9, pp. 1921-1930, September 2003, doi: .
Abstract: Accurately segmenting and quantifying pulmonary nodule structure is a key issue in three-dimensional (3-D) computer-aided diagnosis (CAD) schemes. This paper presents a nodule segmentation method from 3-D thoracic CT images based on a deformable surface model. In this method, first, a statistical analysis of the observed intensity is performed to measure differences between the nodule and other regions. Based on this analysis, the boundary and region information are represented by boundary and region likelihood, respectively. Second, an initial surface in the nodule is manually set. Finally, the deformable surface model moves the initial surface so that the surface provides high boundary likelihood and high posterior segmentation probability with respect to the nodule. For the purpose, the deformable surface model integrates the boundary and region information. This integration makes it possible to cope with inappropriate position or size of an initial surface in the nodule. Using the practical 3-D thoracic CT images, we demonstrate the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_9_1921/_p
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@ARTICLE{e86-d_9_1921,
author={Yoshiki KAWATA, Noboru NIKI, Hironobu OHMATSU, Noriyuki MORIYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={A Deformable Surface Model Based on Boundary and Region Information for Pulmonary Nodule Segmentation from 3-D Thoracic CT Images},
year={2003},
volume={E86-D},
number={9},
pages={1921-1930},
abstract={Accurately segmenting and quantifying pulmonary nodule structure is a key issue in three-dimensional (3-D) computer-aided diagnosis (CAD) schemes. This paper presents a nodule segmentation method from 3-D thoracic CT images based on a deformable surface model. In this method, first, a statistical analysis of the observed intensity is performed to measure differences between the nodule and other regions. Based on this analysis, the boundary and region information are represented by boundary and region likelihood, respectively. Second, an initial surface in the nodule is manually set. Finally, the deformable surface model moves the initial surface so that the surface provides high boundary likelihood and high posterior segmentation probability with respect to the nodule. For the purpose, the deformable surface model integrates the boundary and region information. This integration makes it possible to cope with inappropriate position or size of an initial surface in the nodule. Using the practical 3-D thoracic CT images, we demonstrate the effectiveness of the proposed method.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - A Deformable Surface Model Based on Boundary and Region Information for Pulmonary Nodule Segmentation from 3-D Thoracic CT Images
T2 - IEICE TRANSACTIONS on Information
SP - 1921
EP - 1930
AU - Yoshiki KAWATA
AU - Noboru NIKI
AU - Hironobu OHMATSU
AU - Noriyuki MORIYAMA
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2003
AB - Accurately segmenting and quantifying pulmonary nodule structure is a key issue in three-dimensional (3-D) computer-aided diagnosis (CAD) schemes. This paper presents a nodule segmentation method from 3-D thoracic CT images based on a deformable surface model. In this method, first, a statistical analysis of the observed intensity is performed to measure differences between the nodule and other regions. Based on this analysis, the boundary and region information are represented by boundary and region likelihood, respectively. Second, an initial surface in the nodule is manually set. Finally, the deformable surface model moves the initial surface so that the surface provides high boundary likelihood and high posterior segmentation probability with respect to the nodule. For the purpose, the deformable surface model integrates the boundary and region information. This integration makes it possible to cope with inappropriate position or size of an initial surface in the nodule. Using the practical 3-D thoracic CT images, we demonstrate the effectiveness of the proposed method.
ER -
English
