Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61235
Title: A level set approach to image segmentation with intensity inhomogeneity
Authors: Zhang, K
Zhang, L 
Lam, KM 
Zhang, D 
Keywords: Active contour model
Bias field correction
Intensity inhomogeneity
Level set method
Segmentation
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2016, v. 46, no. 2, 7059203, p. 546-557 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: It is often a difficult task to accurately segment images with intensity inhomogeneity, because most of representative algorithms are region-based that depend on intensity homogeneity of the interested object. In this paper, we present a novel level set method for image segmentation in the presence of intensity inhomogeneity. The inhomogeneous objects are modeled as Gaussian distributions of different means and variances in which a sliding window is used to map the original image into another domain, where the intensity distribution of each object is still Gaussian but better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field with the original signal within the window. A maximum likelihood energy functional is then defined on the whole image region, which combines the bias field, the level set function, and the piecewise constant function approximating the true image signal. The proposed level set method can be directly applied to simultaneous segmentation and bias correction for 3 and 7T magnetic resonance images. Extensive evaluation on synthetic and real-images demonstrate the superiority of the proposed method over other representative algorithms.
URI: http://hdl.handle.net/10397/61235
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2015.2409119
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

116
Last Week
1
Last month
Citations as of Feb 19, 2019

WEB OF SCIENCETM
Citations

95
Last Week
0
Last month
Citations as of Feb 15, 2019

Page view(s)

138
Last Week
1
Last month
Citations as of Feb 17, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.