Active contours with local and global energy based‐on fuzzy clustering and maximum a posterior probability for retinal vessel detection

Wang, Xiancheng; Jiang, Zhi‐Chao; Li, Wei; Zarei, Roozbeh; Huang, Guangyan; Ulhaq, Anwaar; Yin, Xiaoxia; Zhang, Bailing; Shi, Peng; Guo, Mengjiao; He, Jing

doi:10.1002/cpe.5599

Cited by 4 publications

(7 citation statements)

References 40 publications

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“…In this case, given that the tumor can maintain shape integrity, shape compactness and shape smoothness, a new loss function was hereby obtained by introducing two complementary shape constraints into the loss function in [26]. (11) In order to better present the compact shape of the tumor, the equivalence quotient measure CEQM = 4πA/P 2 was proposed, where A and P represent the target shape area and edge length, respectively. The above metric was transformed into the segmentation task, forming the shape compactness constraint [25]: (12) where ρ is the predicted probability image; Ω, the set of all pixels in the image; ∇𝜌𝜌 𝑢𝑢 𝑖𝑖 and ∇𝜌𝜌 𝑣𝑣 𝑖𝑖 , the probability gradients for each pixel i in the horizontal and vertical directions; and o, a hyperparameter for computational stability.…”

Section: Shape-aware Lossmentioning

confidence: 99%

“…The process to separate objects from their respective backgrounds is often known as interactive object selection or interactive segmentation which is commonly required in many image editing and visual analysis workflows [7][8][9]. While recent advanced methods of interactive segmentation focus on the region-based paradigm, more traditional boundarybased methods, such as the binary level set, are still popular in practice as they allow users to have active control over the object boundaries [10][11][12][13]. The main limitation faced by existing boundary-based segmentation methods, however, is that much more user input is often demanded.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Focal Boundary Dice: Improved Breast Tumor Segmentation from MRI Scan

Yin¹,

Jian²,

Shen³

et al. 2023

J. Cancer

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Focal Boundary Dice, a new segmentation evaluation measure, was hereby presented, with the focus on boundary quality and class imbalance. Extensive analysis was carried out across different error types with varied object sizes of imaged tumors from Magnetic Resonance Imaging (MRI) scans, and the results show that Focal Boundary Dice is significantly more adaptive than the standard Focal and Dice measures to boundary errors for imaged tumors from MRI scans and does not over-penalize errors on the division of the boundary, including smaller imaged objects. Based on Boundary Dice, the standard evaluation protocols for tumor segmentation tasks were updated by proposing the Focal Boundary Dice. The contradiction between the target and the background area, and the conflict between the importance and the attention of boundary features were mainly solved. Meanwhile, a boundary attention module was introduced to further extract the tumor edge features. The new quality measure presents several desirable characteristics, including higher accuracy in the selection of hard samples, prediction/ground-truth pairs, and balanced responsiveness with across scales, which jointly make it more suitable for segmentation evaluation than other classification-focused measures such as combined Intersection-over-Union and Boundary binary cross-entropy loss, Boundary binary cross-entropy loss and Shape-aware Loss. The experiments show that the new evaluation metrics allow boundary quality improvements and image segmentation accuracy that are generally overlooked by current Dice-based evaluation metrics and deep learning models. It is expected that the adoption of the new boundary-adaptive evaluation metrics will facilitate the rapid progress in segmentation methods, and further contribute to the improvement of classification accuracy.

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Section: Shape-aware Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Focal Boundary Dice: Improved Breast Tumor Segmentation from MRI Scan

Yin¹,

Jian²,

Shen³

et al. 2023

J. Cancer

View full text Add to dashboard Cite

show abstract

“…4: Update u according to (33), and then normalize it by (35). 5: Smooth u by (36). 6: Check whether the evolution is stationary.…”

Section: E Algorithmmentioning

confidence: 99%

“…[32] and [33] use the fuzzy c-means method to initialize the initial contour and the clustering centers, respectively. In [34]- [36], the coefficient constructed by fuzzy method is invoked as the weight to adjust the evolution of level set function. In addition, many fuzzy ACMs [37]- [52] are constructed by using of pseudo LSF, which is identical to the LSF of traditional ACMs.…”

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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“…To improve the performance of global region‐based LSMs, some local region‐based LSMs, hybrid region‐based LSMs and improved regional LSMs have been proposed. Wang et al 6 proposed a new active contour model by combining the local and global energy based on fuzzy clustering with a weight coefficient. Peng et al 7 developed a new local region‐based active contour model by considering the local image fitting energy to extract the local image information.…”

Section: Introductionmentioning

confidence: 99%

A sweeping optimization algorithm for the global cosine fitting energy image segmentation model

Zou

Chen

et al. 2021

Concurrency and Computation

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Image segmentation plays a pivotal role in image processing. Level set model is a traditional variation image segmentation method. In order to achieve level set evolution equation, the level set energy functionals are minimized with the gradient descent methods and then the partial differential equation (PDE) was solved by finite difference scheme. Slow speed is one of its disadvantages. We propose a sweep optimization algorithm based on global cosine fitting (GCF) energy. Instead of calculating the PDE and the curvature, the proposed sweeping algorithm directly calculates the energy change when a pixel moves from one side of evolving contour to the other. It checks whether the GCF energy is decreased or not. The proposed algorithm has many advantages. For example, independent of initial level set contour positions and parameters, need not consider the Courant Friedrichs Lew condition and the regularization energy term. The proposed algorithm can be easily extended to high dimension image segmentation. The experiments on synthetic images, noise images and real images demonstrate the effectiveness of the proposed sweeping optimization algorithm.

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Active contours with local and global energy based‐on fuzzy clustering and maximum a posterior probability for retinal vessel detection

Cited by 4 publications

References 40 publications

Focal Boundary Dice: Improved Breast Tumor Segmentation from MRI Scan

Focal Boundary Dice: Improved Breast Tumor Segmentation from MRI Scan

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

A sweeping optimization algorithm for the global cosine fitting energy image segmentation model

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