Fast algorithm for hybrid region‐based active contours optimisation

Boutiche, Yamina; Abdesselam, Abdelhamid

doi:10.1049/iet-ipr.2016.0648

Cited by 15 publications

(13 citation statements)

References 23 publications

(40 reference statements)

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“…The authors in [22], proposed an image segmentation model named LBF by considering local image information. The LBF model is capable of segmenting the intensity inhomogeneity images.…”

Section: Related Workmentioning

confidence: 99%

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Active contours driven by modified LoG energy term and optimised penalty term for image segmentation

Biswas

Hazra

2020

IET image process

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“…The authors in [22], proposed an image segmentation model named LBF by considering local image information. The LBF model is capable of segmenting the intensity inhomogeneity images.…”

Section: Related Workmentioning

confidence: 99%

“…The LSMs can be categorised as edge‐based segmentation and region‐based segmentation [17–22]. In edge‐based segmentation, an edge indicator is used to drive the curve towards object boundary.…”

Section: Introductionmentioning

confidence: 99%

Active contours driven by modified LoG energy term and optimised penalty term for image segmentation

Biswas

Hazra

2020

IET image process

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“…(13) θ(x) is a function that adjusts the contribution of the local and global region fitting energies [4].…”

Section: Local and Global Terms In Region Fitting Energymentioning

confidence: 99%

“…The traditional two-phase segmentation divides the image into the foreground and the background with the single level set function, the intensity distribution can be categorized into three classes: global statistical information [1,2], local statistical information [3] and others [4,5]. The multiphase segmentation usually uses multiple level set functions to co-evolve simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Active Contours Driven by Improved Laplace Adaptive Energy in Multiphase Level Set Framework

Yang¹,

Zheng²

2019

dtcse

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This paper presents a novel multiphase level set framework and its corresponding algorithms for image segmentation. The region fitting energies are described with the combination of the global and local distributions. An improved image Laplace term enhances the ability to locate the weak boundary. In order to better deal with the inhomogeneity, the adaptive segmentation energy is proposed to replace the fixed threshold and local Gaussian means and variances are added to get better precision of segmentation results. Experiment shows the proposed model is effective with application to MR images with intensity inhomogeneity.

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“…The available level set approaches [8–10] can be largely classified into edge- and region-based methods. The edge-based methods [11–13] use gradient information to segment images and work well for images with relatively high contrast, while the region-based methods [14–16] use pixel / voxel intensities for segmentation with the assumption that the images have homogeneous intensities. The homogeneity assumption [17] may lead to boundary leakage issues [18–20].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous segmentation and bias field estimation using local fitted images

Wang

Zhu

Sheng

et al. 2018

Pattern Recognition

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Level set methods often suffer from boundary leakage and inadequate segmentation when used to segment images with inhomogeneous intensities. To handle this issue, a novel region-based level set method was developed, in which two different local fitted images are used to construct a hybrid region intensity fitting energy functional. This novel method enables simultaneous segmentation of the regions of interest and estimation of the bias fields from inhomogeneous images. Our experiments on both synthetic images and a publicly available dataset demonstrate the feasibility and reliability of the proposed method.

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Fast algorithm for hybrid region‐based active contours optimisation

Cited by 15 publications

References 23 publications

Active contours driven by modified LoG energy term and optimised penalty term for image segmentation

Active contours driven by modified LoG energy term and optimised penalty term for image segmentation

Hybrid Active Contours Driven by Improved Laplace Adaptive Energy in Multiphase Level Set Framework

Simultaneous segmentation and bias field estimation using local fitted images

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