Image segmentation and bias correction using local inhomogeneous iNtensity clustering (LINC): A region-based level set method

NEUROCOMPUTING

卷:219

Article

Feng, Chaolu^1,2,3  Zhao, Dazhe^1,2,3  Huang, Min^1,4 

[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China

[2] Northeastern Univ, Inst Med Informat Comp & Network Informat Serving, Shenyang 110819, Liaoning, Peoples R China

[3] Northeastern Univ, Minist Educ, Key Lab Med Image Comp, Shenyang 110819, Liaoning, Peoples R China

[4] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Liaoning, Peoples R China

关键词: Level set;    Image segmentation;    Bias correction;    Orthogonal basis function;    Energy minimization;   

DOI  :  10.1016/j.neucom.2016.09.008

来源: Elsevier

【 摘 要 】

Image segmentation is still an open problem due to the existing of intensity inhomogeneity and noise. To accurately segment images with these biases, a local inhomogeneous intensity clustering (LINC) model is proposed. In LINC, a linear combination of a given set of smooth orthogonal basis functions is used to estimate the bias field. A local clustering criterion function is first defined to cluster the nearly homogeneous intensities in a relatively small neighborhood of each pixel. An energy functional is then defined by integrating the function with respect to the neighborhood center. This energy together with a regularization term and an arc length term are incorporated into a variational level set formulation in which de-nosing is implicitly included due to the implied convolution. Image segmentation and bias correction can be simultaneously achieved by updating variables of the final energy functional iteratively till it is stable or a predetermined iteration number is reached. The proposed model LINC has been extensively tested on both synthetic and real images. Experimental results and comparison with state-of-the-art methods demonstrate the advantages of the proposed model in terms of segmentation accuracy, bias field correction, dealing with noise, and robustness to initialization. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

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