Simultaneous segmentation and bias field estimation using local fitted images

Wang, Lei; Zhu, Jianbing; Sheng, Mao; Cribb, Adriena; Shaocheng, Zhu; Pu, Jiantao

doi:10.1016/j.patcog.2017.08.031

Cited by 50 publications

(18 citation statements)

References 35 publications

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“…The performance of the proposed method is demonstrated in the case of two‐phase segmentation first, followed by the multi‐phase segmentation. The proposed AMLLS method is also compared with the LBF [17], LIC [1], MSF [36], LICD [30], LSACM [28], DM [37], LINC [26], HRIF [31], ASACM [33], and FLSAS [34] models on synthetic images, natural images, infrared images, and medical images with intensity inhomogeneity.…”

Section: Resultsmentioning

confidence: 99%

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Adaptive multilayer level set method for segmenting images with intensity inhomogeneity

Huang

Zhang

et al. 2019

IET image process

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The level set method based on bias correction can segment images with gentle intensity inhomogeneity effectively. However, most level set methods fail to segment severe inhomogeneous images due to the use of fixed scale clustering criterion. To deal with this problem, an adaptive multilayer level set method is proposed to segment images with severe intensity inhomogeneity. First, an improved global adaptive scale operator and a local adaptive scale operator are designed to adaptively adjust the scale of clustering kernel function according to the degree of intensity inhomogeneity. Then, an adaptive multilayer level set structure is constructed with the two designed scale operators. The number of layers and the scale of each layer in the multilayer structure are adaptively determined based on the degree of intensity inhomogeneity, which not only provides appropriate candidate scales in each pixel but also allows the model to detect global contrast information. With the dual minimisation method, image segmentation and bias correction can be achieved simultaneously. In addition, a hybrid bias field initialisation procedure is proposed to enhance the robustness of the proposed method. Experimental results demonstrate the effectiveness and robustness of the proposed method in segmenting images with intensity inhomogeneity.

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Section: Resultsmentioning

confidence: 99%

“…This field is referred to as the bias field. Some bias correction‐based methods for image segmentation have been proposed [1, 25–31]. Li et al proposed a multiplicative intrinsic component optimisation (MICO) model [25], which utilised a set of smooth basis functions to estimate the bias field.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multilayer level set method for segmenting images with intensity inhomogeneity

Huang

Zhang

et al. 2019

IET image process

View full text Add to dashboard Cite

show abstract

“…Such an approach completely avoids the need for modelling the intensity inhomogeneity function separately. In recent years, non‐parametric and FCM‐based clustering methods are proposed [4–22].…”

Section: Introductionmentioning

confidence: 99%

Novel fuzzy clustering‐based bias field correction technique for brain magnetic resonance images

Mishro¹,

Agrawal²,

Panda³

et al. 2020

IET Image Processing

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“…Image segmentation [1] aims to partition an image into meaningful subregions. There have been numerous approaches developed for this purpose [2–4]. Among the available schemes, active contour models [5–7] attract considerable attention and are able to segment target regions with reasonable accuracy.…”

Section: Introductionmentioning

confidence: 99%

Active contours driven by edge entropy fitting energy for image segmentation

et al. 2018

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Active contour models have been widely used for image segmentation purposes. However, they may fail to delineate objects of interest depicted on images with intensity inhomogeneity. To resolve this issue, a novel image feature, termed as local edge entropy, is proposed in this study to reduce the negative impact of inhomogeneity on image segmentation. An active contour model is developed on the basis of this feature, where an edge entropy fitting (EEF) energy is defined with the combination of a redesigned regularization term. Minimizing the energy in a variational level set formulation can successfully drive the motion of an initial contour curve towards optimal object boundaries. Experiments on a number of test images demonstrate that the proposed model has the capability of handling intensity inhomogeneity with reasonable segmentation accuracy.

show abstract

Simultaneous segmentation and bias field estimation using local fitted images

Cited by 50 publications

References 35 publications

Adaptive multilayer level set method for segmenting images with intensity inhomogeneity

Adaptive multilayer level set method for segmenting images with intensity inhomogeneity

Novel fuzzy clustering‐based bias field correction technique for brain magnetic resonance images

Active contours driven by edge entropy fitting energy for image segmentation

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