IEICE TRANSACTIONS on Information
Medical Image Segmentation Using Level Set Method with a New Hybrid Speed Function Based on Boundary and Region Segmentation
Summary :
This paper presents a new hybrid speed function needed to perform image segmentation within the level-set framework. The proposed speed function uses both the boundary and region information of objects to achieve robust and accurate segmentation results. This speed function provides a general form that incorporates the robust alignment term as a part of the driving force for the proper edge direction of an active contour, an active region term derived from the region partition scheme, and the smoothing term for regularization. First, we use an external force for active contours as the Gradient Vector Flow field. This is computed as the diffusion of gradient vectors of a gray level edge map derived from an image. Second, we partition the image domain by progressively fitting statistical models to the intensity of each region. Here we adopt two Gaussian distributions to model the intensity distribution of the inside and outside of the evolving curve partitioning the image domain. Third, we use the active contour model that has the computation of geodesics or minimal distance curves, which allows stable boundary detection when the model's gradients suffer from large variations including gaps or noise. Finally, we test the accuracy and robustness of the proposed method for various medical images. Experimental results show that our method can properly segment low contrast, complex images.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.8 pp.2133-2141
- Publication Date
- 2012/08/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.2133
- Type of Manuscript
- PAPER
- Category
- Biological Engineering
Authors
Keyword
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Jonghyun PARK, Soonyoung PARK, Wanhyun CHO, "Medical Image Segmentation Using Level Set Method with a New Hybrid Speed Function Based on Boundary and Region Segmentation" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 8, pp. 2133-2141, August 2012, doi: 10.1587/transinf.E95.D.2133.
Abstract: This paper presents a new hybrid speed function needed to perform image segmentation within the level-set framework. The proposed speed function uses both the boundary and region information of objects to achieve robust and accurate segmentation results. This speed function provides a general form that incorporates the robust alignment term as a part of the driving force for the proper edge direction of an active contour, an active region term derived from the region partition scheme, and the smoothing term for regularization. First, we use an external force for active contours as the Gradient Vector Flow field. This is computed as the diffusion of gradient vectors of a gray level edge map derived from an image. Second, we partition the image domain by progressively fitting statistical models to the intensity of each region. Here we adopt two Gaussian distributions to model the intensity distribution of the inside and outside of the evolving curve partitioning the image domain. Third, we use the active contour model that has the computation of geodesics or minimal distance curves, which allows stable boundary detection when the model's gradients suffer from large variations including gaps or noise. Finally, we test the accuracy and robustness of the proposed method for various medical images. Experimental results show that our method can properly segment low contrast, complex images.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.2133/_p
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@ARTICLE{e95-d_8_2133,
author={Jonghyun PARK, Soonyoung PARK, Wanhyun CHO, },
journal={IEICE TRANSACTIONS on Information},
title={Medical Image Segmentation Using Level Set Method with a New Hybrid Speed Function Based on Boundary and Region Segmentation},
year={2012},
volume={E95-D},
number={8},
pages={2133-2141},
abstract={This paper presents a new hybrid speed function needed to perform image segmentation within the level-set framework. The proposed speed function uses both the boundary and region information of objects to achieve robust and accurate segmentation results. This speed function provides a general form that incorporates the robust alignment term as a part of the driving force for the proper edge direction of an active contour, an active region term derived from the region partition scheme, and the smoothing term for regularization. First, we use an external force for active contours as the Gradient Vector Flow field. This is computed as the diffusion of gradient vectors of a gray level edge map derived from an image. Second, we partition the image domain by progressively fitting statistical models to the intensity of each region. Here we adopt two Gaussian distributions to model the intensity distribution of the inside and outside of the evolving curve partitioning the image domain. Third, we use the active contour model that has the computation of geodesics or minimal distance curves, which allows stable boundary detection when the model's gradients suffer from large variations including gaps or noise. Finally, we test the accuracy and robustness of the proposed method for various medical images. Experimental results show that our method can properly segment low contrast, complex images.},
keywords={},
doi={10.1587/transinf.E95.D.2133},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Medical Image Segmentation Using Level Set Method with a New Hybrid Speed Function Based on Boundary and Region Segmentation
T2 - IEICE TRANSACTIONS on Information
SP - 2133
EP - 2141
AU - Jonghyun PARK
AU - Soonyoung PARK
AU - Wanhyun CHO
PY - 2012
DO - 10.1587/transinf.E95.D.2133
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2012
AB - This paper presents a new hybrid speed function needed to perform image segmentation within the level-set framework. The proposed speed function uses both the boundary and region information of objects to achieve robust and accurate segmentation results. This speed function provides a general form that incorporates the robust alignment term as a part of the driving force for the proper edge direction of an active contour, an active region term derived from the region partition scheme, and the smoothing term for regularization. First, we use an external force for active contours as the Gradient Vector Flow field. This is computed as the diffusion of gradient vectors of a gray level edge map derived from an image. Second, we partition the image domain by progressively fitting statistical models to the intensity of each region. Here we adopt two Gaussian distributions to model the intensity distribution of the inside and outside of the evolving curve partitioning the image domain. Third, we use the active contour model that has the computation of geodesics or minimal distance curves, which allows stable boundary detection when the model's gradients suffer from large variations including gaps or noise. Finally, we test the accuracy and robustness of the proposed method for various medical images. Experimental results show that our method can properly segment low contrast, complex images.
ER -
English
