Active contour image segmentation model with de‐hazing constraints

Ali, Haider; Sher, Awal; Saeed, M.; Rada, Lavdie

doi:10.1049/iet-ipr.2018.5987

Cited by 6 publications

(5 citation statements)

References 38 publications

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“…In this Experiment, we compare the proposed KLIF model with MCV [30], GLFIF [33], ACDC [20] model for mixed noisy images, where MCV is a multi‐scale variational level set model; GLFIF is a hybrid soft segmentation model that combines the local‐global region based information and the fuzzy membership; ACDC incorporates the atmospheric veil estimation and the locally computed denoising constrained surfaces into a variational level set model. The experiment results are shown in Figure 12.…”

Section: The Experimental Resultsmentioning

confidence: 99%

“…Variational level set models can mainly be categorised into two distinct classes: edge-based models [1][2][3][4][5][6][7][8][9][10][11] and region-based models [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. The edge-based models, such as snakes [1], geodesic active contour [8], gradient vector flow [10], use gradient information of image to define the velocity of the active contour.…”

Section: Introductionmentioning

confidence: 99%

“…CV model can effectively segment piecewise constant (PC) images, but fails for the smooth images with intensity inhomogeneity because it uses a constant fitting term in the model. To segment smooth images with intensity inhomogeneity, some improved CV-based model have been proposed [14][15][16][17][18][19][20][21][22]. Li et al [17] proposed a local binary fitting (LBF) model to segment piecewise smooth (PS) images.…”

Section: Introductionmentioning

confidence: 99%

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A variational level set model with kernel metric induced local image fitting energy

Yan

Tang

Ren

et al. 2022

IET Image Processing

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Active contour based methods are effective models for image segmentation. However, they always suffer from the limited performance due to the presence of noise and intensity inhomogeneity. To solve this problem, a kernel metric induced local image fitting (KLIF) variational model is proposed in this paper. Firstly, a kernel metric induced local fitting image (KLFI) is introduced by minimising a kernel metric based energy. The combination of the kernel metric and the local fitting image enables the model to be more robust to the noise and intensity inhomogeneity. And then, using the KLFI, a variational level set model that is a squared l 2 distance between the KLFI and the original image is constructed. Two regularisation terms are employed in the model to keep the level set function to be stable during the evolution. At last, an alternating iterative algorithm combining with fixed-point iteration and gradient descent of three-step time-splitting is introduced to solve the proposed model. The experimental results show the effectiveness of the proposed model for image segmentation in the presence of noise and intensity inhomogeneity, and demonstrate the competitive performance over several state-of-the-art variational models in term of accuracy and robustness.

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Section: The Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A variational level set model with kernel metric induced local image fitting energy

Yan

Tang

Ren

et al. 2022

IET Image Processing

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show abstract

“…However, determining the initial curve plays a vital role in the segmentation of desired region. [ 20 21 22 23 ] If the initial curve is far from the region which we want to extract, the segmentation result will not be proper. [ 24 25 26 ] Therefore, here, we have used the image histogram for initial curve detection.…”

Section: Introductionmentioning

confidence: 99%

Constructing Multiwavelet-based Shearlets and using Them for Automatic Segmentation of Noisy Brain Images Affected by COVID-19

Aghazadeh,

Moradi,

Noras

2023

Journal of Medical Signals &Amp; Sensors

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Backgorund: Nowadays, everybody's life is dominated by COVID-19, which might have been the source of severe acute respiratory syndrome coronavirus 2. This virus disrupts the lungs first of all. Recently, it has been found that coronavirus may affect the brain. Because all body actions rely on the brain, hence investigating its healthy is an essential item in coronavirus effects. Method: Brain image segmentation can be helpful in the detection of the regions damaged by the effects of coronavirus. Since every image given by photography devices may have noises, therefore, first of all, the brain magnetic resonance angiography (MRA) images must be denoised for best investigation. In the present paper, we have presented the construction of multishearlets based on multiwavelets for the first time and have used them for the purpose of denoising. Multiwavelets have some advantages to wavelets. Therefore, we have used them in the shearlet system to expand the properties of multiwavelets in all directions. After denoising, we have proposed a scheme for the automatic characterization of the initial curve in the active contour model for segmentation. Detecting the initial curve is a challenging task in active contour-based segmentation because detecting an initial curve far from the desired region can lead to unfavorable results. Results: The results show the performance of using multishearlets in detecting affected regions by COVID-19. Using multishearlets has led to the high value of peak signal-to-noise ratio and Structural similarity index measure in comparison with original shearlets. Original shearlets are constructed from wavelets whereas we have constructed multishearlets from multiwavelets. Conclusion: The results show that multishearlets can neutralize the effect of noise in MRA images in a good way rather than shearlets. Moreover, the proposed scheme for segmentation can lead to 0.99 accuracy.

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“…Usually, the internal force comes from the geometrical properties of the contours and the external force is computed from the image data so that the snake will conform to an object boundary or other desired features within an image. The active contour model has been one of the most successful methods for region of interest (ROI) segmentation, and it can be grouped into two categories: parametric active contours [1] and geometric active contours [2][3][4][5][6][7][8][9][10][11][12]. Although deep learning-based methods launch an upsurge of image segmentation at present [13][14][15][16][17], the active contours are still an active topic (e.g., [18][19][20][21][22][23][24][25][26][27][28][29]), and we also focus on parametric active contours in this study.…”

Section: Introductionmentioning

confidence: 99%

Image segmentation using active contours with image structure adaptive gradient vector flow external force

Wang,

Dang,

Liu

et al. 2023

Front. Appl. Math. Stat.

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IntroductionGradient vector flow (GVF) has been proven as an effective external force for active contours. However, its smoothness constraint does not take the image structure into account, such that the GVF diffusion is isotropic and cannot preserve weak edges well.MethodsIn this article, an image structure adaptive gradient vector flow (ISAGVF) external force is proposed for active contours. In the proposed ISAGVF model, the smoothness constraint is first reformulated in matrix form, and then the image structure tensor is incorporated. As the structure tensor characterizes the image structure well, the proposed ISAGVF model can be adaptive to image structure, and the ISAGVF snake performs well on weak edge preservation and deep concavity convergence while possessing some other desirable properties of the GVF snake, such as enlarged capture range and insensitivity to initialization.ResultsExperiments on synthetic and real images manifest these properties of the ISAGVF snake.

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Active contour image segmentation model with de‐hazing constraints

Cited by 6 publications

References 38 publications

A variational level set model with kernel metric induced local image fitting energy

A variational level set model with kernel metric induced local image fitting energy

Constructing Multiwavelet-based Shearlets and using Them for Automatic Segmentation of Noisy Brain Images Affected by COVID-19

Image segmentation using active contours with image structure adaptive gradient vector flow external force

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