A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity

Ali, Haider; Badshah, Noor; Chen, Ke; Khan, Gulzar Ali

doi:10.1016/j.patcog.2015.08.022

Cited by 67 publications

(44 citation statements)

References 22 publications

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“…The watershed and threshold algorithm was combined for the efficient segmentation of tumors in the MR images of brain [95]. The region growing and level set algorithms along with artificial neural network were used for the segmentation and classification of breast tumors [96]. The local Chan-Vese model and finite Gaussian mixture model was combined such that it yields efficient results for low contrast and noisy images [97].…”

Section: Hybrid Approachesmentioning

confidence: 99%

A voyage on medical image segmentation algorithms

Sn¹,

A²,

Muthukumar³

et al. 2018

biomedicalresearch

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Image segmentation is the process of subdividing an image into eloquent regions that are consistent and homogeneous in some characteristics. Image segmentation is indeed a vital process in the early diagnosis of abnormalities and treatment planning. The segmentation algorithms are employed to extract the anatomical structures and anomalies from medical images. The segmentation algorithms can be categorized into three generations. The first generation algorithms are based on threshold, seed point selection and edge tracing methods. The second generation algorithms incorporate uncertainty and optimization models and the third generation algorithms considers the prior information in segmentation process. This review work discusses and conceptualizes the various segmentation algorithms, which are in correlation with medical images and adduce the result of some of the significant algorithms in each generation. Moreover, the proposed work does spell out the pros and cons of the algorithms for computer aided analysis. In extension, this literature review indeed paves an ample platform to the researchers for better understanding of various segmentation techniques and its characteristics for medical images.

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Section: Hybrid Approachesmentioning

confidence: 99%

A voyage on medical image segmentation algorithms

Sn¹,

A²,

Muthukumar³

et al. 2018

biomedicalresearch

View full text Add to dashboard Cite

show abstract

“…The Geodesic-Aided Chan-Vese (GACV) model [27] consists of a GAC model and a Chan-Vese model, which combines the edge information and the region information and can selectively deal with local and global segmentation. The variational hybrid model [28] includes two fitted terms based on edges and regions, which was used to balance the average intensities of the interior and exterior regions. Li et al [29] proposed a distance regularization level set evolution (DRLSE) model with a distance regularization term and a data term, which drove level set evolution toward forward-and-backward (FAB) diffusion.…”

Section: Introductionmentioning

confidence: 99%

A Novel Active Contour Model Guided by Global and Local Signed Energy-Based Pressure Force

et al. 2020

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Active contour models (ACMs) have been widely applied in the field of image segmentation. However, it is still very challenging to construct an efficient ACM to segment images with intensity inhomogeneity. In this paper, a novel ACM guided by global and local signed energy-based pressure force (GLSEPF) is proposed. First, by computing the energy difference between the inner and outer energies of the evolution curve, a global signed energy-based pressure force (GSEPF) is designed, which can improve the robustness to initial curves. Second, a local signed energy-based pressure force (LSEPF) is introduced by computing the pixel-by-pixel energy difference within local neighborhood region, which can handle images with intensity inhomogeneity and noise. Finally, the global image information and the local energy information are used for the global and local force propagation functions, respectively. The global and local variances are used to automatically balance the weights of the GSEPF and the LSEPF, which can solve the problem of setting parameters. Meanwhile, a regularization term and a penalty term are applied to avoid the re-initialization process during iterations and smooth the level set function. Experimental results on different types of images demonstrate that the proposed model is more robust than the popular region-based and mixed ACMs for segmenting images with intensity inhomogeneity and noise.

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“…Otherwise, the model cannot effectively segment images with intensity inhomogeneity. In [25], the multiplicative and difference images are used to formulate a new hybrid ACM. However, finding an appropriate method to process the original images is a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Active Contour Model Using Fractional-Order Diffusion Based Edge Indicator and Fuzzy Local Fitted Image

Zhang

Wang

2020

IEEE Access

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Local region-based active contour models (ACMs) can effectively segment images corrupted by intensity inhomogeneity, however, they always converge to local minimum and are sensitive to the initial position of contour. In this paper, a novel fuzzy ACM is proposed to tackle these problems. In order to deal with intensity inhomogeneity, the fuzzy local fitted image is first defined and utilized for constructing a local-region based fuzzy energy term, which is minimized in a variational level set framework to accurately segment inhomogeneous images. Second, the fractional-order diffusion based edge indicator is used to scale the local fuzzy energy term to reduce the effect of intensity inhomogeneity. Third, the fuzzy signed pressure force (FSPF) function defined by local image information is used for constructing the weighted area term to further improve the accuracy of the developed model. Finally, the global FSPF is formulated and used as an adaptive force, which can drive the level set function (LSF) to adaptively move up or down according to image intensity information. Therefore, the initial contour can be initialized as a constant function, which eliminates the problem caused by contour initialization. Moreover, the global FSPF makes the proposed model not easy to fall into local minimum. The results of experiments on synthetic and real images validate the accuracy of the proposed model for inhomogeneous image segmentation. INDEX TERMS Active contour model, inhomogeneous image segmentation, fuzzy local fitted image, edge indicator, FSPF function.

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A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity

Cited by 67 publications

References 22 publications

A voyage on medical image segmentation algorithms

A voyage on medical image segmentation algorithms

A Novel Active Contour Model Guided by Global and Local Signed Energy-Based Pressure Force

Fuzzy Active Contour Model Using Fractional-Order Diffusion Based Edge Indicator and Fuzzy Local Fitted Image

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