A wavelet-frame based image force model for active contouring algorithms

Wu, Hsien-Hsun Steven; Liu, Jyh-Charn; Chui, Chee-Kong

doi:10.1109/83.877221

Cited by 18 publications

(2 citation statements)

References 9 publications

(8 reference statements)

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“…Second multi-resolution strategy has a more robust and strong ability to reduce the effect caused by noise. Wu et al 20 proposed a new directional image force for active contouring based on wavelet frames, which can help for improving the immunity of AC models to noise. Ma et al 21 utilized a curvelet-based parametric AC model for multi-scale image segmentation and tracking of geophysical fluids.…”

Section: Introductionmentioning

confidence: 99%

Active contour for noisy image segmentation based on contourlet transform

Chen

Yang

2012

J. Electron. Imaging

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Active contour is one of the most successful variational models in image segmentation, pattern analysis, and computer vision. However, traditional active contour models not only require much expensive computation but are very sensitive to noise. We propose a scheme for noisy image segmentation integrating the active contour model with the contourlet transform, an optimal sparse representation of an image. Having reconstructed all the scale maps, we downsample the last but one scale map twice. Then, we apply the active contour model on the coarsest scale map and take the segmentation results as the initial curves for the finer scale map. Experiments have demonstrated that our proposed method can yield desired segmentation results both in real and synthetic images.

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Section: Introductionmentioning

confidence: 99%

Active contour for noisy image segmentation based on contourlet transform

Chen

Yang

2012

J. Electron. Imaging

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“…Many approaches have been proposed to solve this model [3][4][5][6][7][8]. For example, the greedy algorithm has a high efficiency but it cannot guarantee the global convergence.…”

Section: Introductionmentioning

confidence: 99%

Finite grade pheromone ant colony optimization for image segmentation

Yuanjing

Liangjun

2008

Opto-Electronics Review

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By combining the decision process of ant colony optimization (ACO) with the multistage decision process of image segmentation based on active contour model (ACM), an algorithm called finite grade ACO (FACO) for image segmentation is proposed. This algorithm classifies pheromone into finite grades and updating of the pheromone is achieved by changing the grades and the updated quantity of pheromone is independent from the objective function. The algorithm that provides a new approach to obtain precise contour is proved to converge to the global optimal solutions linearly by means of finite Markov chains. The segmentation experiments with ultrasound heart image show the effectiveness of the algorithm. Comparing the results for segmentation of left ventricle images shows that the ACO for image segmentation is more effective than the GA approach and the new pheromone updating strategy appears good time performance in optimization process.

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Wavelets in Medical Image Processing: Denoising, Segmentation, and Registration

Jin

Angelini

Laine

Handbook of Biomedical Image Analysis

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A wavelet-frame based image force model for active contouring algorithms

Cited by 18 publications

References 9 publications

Active contour for noisy image segmentation based on contourlet transform

Active contour for noisy image segmentation based on contourlet transform

Finite grade pheromone ant colony optimization for image segmentation

Wavelets in Medical Image Processing: Denoising, Segmentation, and Registration

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