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A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction
Joint Authors
Zhao, Dazhe
Li, Wei
Yu, Kun
Feng, Chaolu
Yang, Jinzhu
Lou, Chunhui
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-01
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities.
A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions.
First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model.
Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively.
Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively.
Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories.
American Psychological Association (APA)
Feng, Chaolu& Yang, Jinzhu& Lou, Chunhui& Li, Wei& Yu, Kun& Zhao, Dazhe. 2020. A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1139576
Modern Language Association (MLA)
Feng, Chaolu…[et al.]. A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1139576
American Medical Association (AMA)
Feng, Chaolu& Yang, Jinzhu& Lou, Chunhui& Li, Wei& Yu, Kun& Zhao, Dazhe. A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1139576
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139576