Hybrid active contour model driven by optimized local pre-fitting image energy for fast image segmentation

Yan, Xin; Weng, Guirong

doi:10.1016/j.apm.2021.09.002

Cited by 27 publications

(15 citation statements)

References 25 publications

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“…Geodesic active contour (GAC) proposed by Caselles et al is a model based on curve evolution theory and level set method. It can deal with topological structure changes of curve movement freely without any external control conditions, and it is the most widely used edge model [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

Construction of Biologic Microscopic Image Segmentation Model Based on Smoothing of Fourth-Order Partial Differential Equation

2022

Scanning

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In order to solve the problem of microscopic image noise, a biological microscopic image segmentation model based on the smoothing of the fourth-order partial differential equation was proposed. Based on the functional description of image smoothness by directional curvature mode value, a fourth-order PDE image denoising model is derived, which can effectively reduce noise while preserving edges. The result of this method is piecewise linear image, and the gradient at the edge of the target has a step. Using the feature of noise reduction, a new geodesic active contour model is proposed. The experiment result shows that when the variance of Gaussian white noise is 15, the enhancement and denoising effects of the proposed method are 80.35% and 69.84 higher than those of the original vibration filtering method and L. Alvarez method. In terms of time, the proposed method is 1.3075 seconds slower than the original vibration filtering method and 17.5754 seconds faster than the L. Alvarez method. When the variance of Gaussian white noise is 25, the enhancement and denoising effects of the proposed method are 97.79% and 81.16 higher than those of the original vibration filtering method and L. Alvarez method. In terms of time, the proposed method is 1.3246 seconds slower than the original vibration filtering method and 17.5796 seconds faster than the L. Alvarez method. Conclusion. The new model is not only stable but also has strong ability of contour extraction and fast convergence.

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Section: Methodsmentioning

confidence: 99%

Construction of Biologic Microscopic Image Segmentation Model Based on Smoothing of Fourth-Order Partial Differential Equation

2022

Scanning

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“…Taking the original image as input, the final solution is obtained when t ⟶∞. In order for the image not to be oversmoothed, the iteration must be terminated with a time limit [ 16 ].…”

Section: Methodsmentioning

confidence: 99%

Construction of Computer Microscope Image Segmentation Model Based on Fourth-Order Partial Differential Equation Smoothing

2022

Scanning

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In order to solve the problem of image noise, the author proposes a computer microscope image segmentation model based on the smoothing of fourth-order partial differential equations. On the basis of the functional describing the smoothness of the image by the directional curvature modulus, the author deduces a fourth-order partial differential equation (PDE) image noise reduction model, while effectively reducing noise, the edges are well preserved. The processing result of this method is a piecewise linear image, and there is a step in the gradient at the edge of the target. Taking advantage of this feature of the noise reduction results, the author proposes a new geodesic active contour model. The experimental results show that the reference method directly segments the results, iterates 10 times, and takes 160.721 seconds. Using the noise reduction model in the paper to preprocess and then using the reference method to segment the result, iterating 8 times, it takes 32.347 seconds. Conclusion. The new model is not only stable but also has strong contour extraction ability and fast convergence speed.

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“…The energy sign function can adaptively choose the motion direction based on picture data. According to experimental findings, the model is quite effective at segmenting pictures [18]. Results on photos of various objects have demonstrated that the model is more robust than existing models.…”

Section: Engineering Applications Of Artificial Intelligencementioning

confidence: 91%

Image Segmentation Using Hybrid Optimization Algorithms: Review

Hemeida¹,

Yahya

Al-Sanary

2022

Aswan University Journal of Sciences and Technology

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Image segmentation, often based on the properties of image pixels, is a widely used method in digital image processing and analysis to divide an image into multiple parts or areas. The main goal of image segmentation is to simplify the image for easier analysis. It would be very difficult to implement computer vision without performing image segmentation. An important component of computer vision is image segmentation, which has numerous commercial applications. Google and others Image-based search engines use image segmentation algorithms to identify the objects in your image and match their results to the relevant images they find to present you with search results.

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Hybrid active contour model driven by optimized local pre-fitting image energy for fast image segmentation

Cited by 27 publications

References 25 publications

Construction of Biologic Microscopic Image Segmentation Model Based on Smoothing of Fourth-Order Partial Differential Equation

Construction of Biologic Microscopic Image Segmentation Model Based on Smoothing of Fourth-Order Partial Differential Equation

Construction of Computer Microscope Image Segmentation Model Based on Fourth-Order Partial Differential Equation Smoothing

Image Segmentation Using Hybrid Optimization Algorithms: Review

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