Fast Region-based Active Contour Model Driven by Local Signed Pressure Force

Abdallah, Azizi; Elkourd, Kaouther

doi:10.5565/rev/elcvia.794

Cited by 7 publications

(7 citation statements)

References 19 publications

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“…This type of active contours at summer implemented to cure the problems arising from the method of explicit active contours (Snakes) [3,11].…”

Section: Implicit Active Contoursmentioning

confidence: 99%

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Active Contours with Term of Smoothing: Application in Medical Imaging

Dib

2020

Wseas Transactions on Signal Processing

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The segmentation of images is to group the different pixels of the image into classes, each classgrouping similar pixels in the sense of a given criteria. The segmentation of images is, in general, verydifficult to achieve because the natural images are various, complex and the way of perceiving them variesaccording to the individuals. Because of this difficulty, several approaches have been proposed. Theapproach addressed in this article is the technique of active contours. We focus on implicit active contours;we propose an improvement for the model of Chan&Vese. The application of our method on medicalimages shown they can be a tool for the image segmentation, and thus provide an aid to diagnosis for thepractitioner.

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“…This type of active contours at summer implemented to cure the problems arising from the method of explicit active contours (Snakes) [3,11].…”

Section: Implicit Active Contoursmentioning

confidence: 99%

“…(9) To regularize the zero level contour of Φ, we also need the length of the zero level of Φ, which is given by (10) Now, we define the entire energy functional (11) where μ and ρ are positive constants.…”

Section: Formulation Of the Model With The Levels Setsmentioning

confidence: 99%

Active Contours with Term of Smoothing: Application in Medical Imaging

Dib

2020

Wseas Transactions on Signal Processing

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“…The ACM based on Hessian matrix (ACM-HM) [32] replaced the derivative of the LSF in Zhang's model [30] with the eigenvalue information of Hessian matrix, which could effectively handle with images with blurred boundaries. More ACMs were found in the literature [33]- [35] by incorporating different image information into the SPF, such as the global image information model (GSRPF) [33], the local signed pressure force (LSPF) model [34], the SPF-LIF model [35], the global and local weighted signed pressure force (GL-SPF) model [36], and weighted hybrid region-based signed pressure force (WHRSPF) [37]. However, the SPFs in these models utilized the global image information and could not extract desired objects from images with intensity InH.…”

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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“…These models are very sensitive to initialization and outliers (noise) in the image. While region based models use region statistics (like least square data fitting) for detection of region of interest in images [3].…”

Section: Introductionmentioning

confidence: 99%

New Local Region Based Model for the Segmentation of Medical Images

et al. 2020

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Segmentation of images having inhomogeneous intensities is always challenging. In this paper, we propose a model based on new local data statistics using local means and variances for detection of region of interest in medical images suffered from intensity inhomogeneity. This is done by introducing a new probability density function based on coefficient of variation, which is a best measure for inhomogeneous data. The new energy functional in the proposed model is then expressed in terms of level set function and is minimized for optimal energy. Minimization of the energy will lead to a partial differential equation, which is solved by using well known explicit method. Results of the proposed model are compared with other state of the art models and found that the proposed model outperform other existing models. Comparison is given in both qualitative and quantitative way. Furthermore, the proposed model is tested on different type of medical images like MRI, CT, Mammogram and skin lesion etc. INDEX TERMS Gaussian processes, image segmentation, intensity inhomogeneity, level set method, variational techniques.

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Fast Region-based Active Contour Model Driven by Local Signed Pressure Force

Cited by 7 publications

References 19 publications

Active Contours with Term of Smoothing: Application in Medical Imaging

Active Contours with Term of Smoothing: Application in Medical Imaging

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

New Local Region Based Model for the Segmentation of Medical Images

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