An unconditionally stable numerical method for bimodal image segmentation

Yibao Li; Junseok Kim

doi:10.1016/j.amc.2012.09.038

An unconditionally stable numerical method for bimodal image segmentation

Yibao Li, Junseok Kim

Research output: Contribution to journal › Article › peer-review

28 Citations (Scopus)

Abstract

In this paper, we propose a new level set-based model and an unconditionally stable numerical method for bimodal image segmentation. Our model is based on the Lee-Seo active contour model. The numerical scheme is semi-implicit and solved by an analytical method. The unconditional stability of the proposed numerical method is proved analytically. We demonstrate performance of the proposed image segmentation algorithm on several synthetic and real images to confirm the efficiency and stability of the proposed method.

Original language	English
Pages (from-to)	3083-3090
Number of pages	8
Journal	Applied Mathematics and Computation
Volume	219
Issue number	6
DOIs	https://doi.org/10.1016/j.amc.2012.09.038
Publication status	Published - 2012 Nov 25

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2011-0023794 ).

Keywords

Chan-Vese model
Energy minimization
Image segmentation
Lee-Seo model
Level set model
Unconditional stability

ASJC Scopus subject areas

Computational Mathematics
Applied Mathematics

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10.1016/j.amc.2012.09.038

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title = "An unconditionally stable numerical method for bimodal image segmentation",

abstract = "In this paper, we propose a new level set-based model and an unconditionally stable numerical method for bimodal image segmentation. Our model is based on the Lee-Seo active contour model. The numerical scheme is semi-implicit and solved by an analytical method. The unconditional stability of the proposed numerical method is proved analytically. We demonstrate performance of the proposed image segmentation algorithm on several synthetic and real images to confirm the efficiency and stability of the proposed method.",

keywords = "Chan-Vese model, Energy minimization, Image segmentation, Lee-Seo model, Level set model, Unconditional stability",

author = "Yibao Li and Junseok Kim",

note = "Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2011-0023794 ).",

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doi = "10.1016/j.amc.2012.09.038",

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AU - Kim, Junseok

N1 - Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2011-0023794 ).

PY - 2012/11/25

Y1 - 2012/11/25

N2 - In this paper, we propose a new level set-based model and an unconditionally stable numerical method for bimodal image segmentation. Our model is based on the Lee-Seo active contour model. The numerical scheme is semi-implicit and solved by an analytical method. The unconditional stability of the proposed numerical method is proved analytically. We demonstrate performance of the proposed image segmentation algorithm on several synthetic and real images to confirm the efficiency and stability of the proposed method.

AB - In this paper, we propose a new level set-based model and an unconditionally stable numerical method for bimodal image segmentation. Our model is based on the Lee-Seo active contour model. The numerical scheme is semi-implicit and solved by an analytical method. The unconditional stability of the proposed numerical method is proved analytically. We demonstrate performance of the proposed image segmentation algorithm on several synthetic and real images to confirm the efficiency and stability of the proposed method.

KW - Chan-Vese model

KW - Energy minimization

KW - Image segmentation

KW - Lee-Seo model

KW - Level set model

KW - Unconditional stability

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M3 - Article

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SN - 0096-3003

VL - 219

SP - 3083

EP - 3090

JO - Applied Mathematics and Computation

JF - Applied Mathematics and Computation

IS - 6

ER -

An unconditionally stable numerical method for bimodal image segmentation

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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