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A Novel Hybrid level set model for Non-Rigid Object Contour Tracking

IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 15-29页

作者： Cai, Qing Liu, Huiying Qian, Yiming Zhou, Sanping Wang, Jinjun Yang, Yee-Hong Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Guangdong Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230026 Anhui Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Guangdong Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Shaanxi Peoples R China Univ Manitoba Dept Comp Sci Winnipeg MB R3T 2N2 Canada Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China Univ Alberta Dept Comp Sci Edmonton AB T6G 2S4 Canada

Most existing trackers use bounding boxes for object tracking. However, the background contained in the bounding box inevitably decreases the accuracy of the target model, which affects the performance of the tracker and is particularly pronounced for non-rigid objects. To address the above issue, this paper proposes a novel hybrid level set model, which can robustly address the issue of topology changing, occlusions and abrupt motion in non-rigid object tracking by accurately tracking the object contour. In particular, an appearance model is first obtained by repeatedly training and relabeling the initial labeled frame using competing one-class SVMs. Then, by integrating the trained appearance model, an edge detector and image spatial information into the level set model, a new hybrid level set model is presented, which accurately locates the object contour and feeds back to the competing one-class SVMs to update the appearance model of the next frame. In addition, a motion model is defined to predict the accurate location of the object when occlusion and abrupt motion occur in the next frame. Finally, the experimental results on state-of-the-art benchmarks demonstrate the feasibility and effectiveness of the proposed model and the superiority of the proposed method over existing trackers in terms of accuracy and robustness.

关键词： level set model one-class SVM particle filter non-rigid object contour tracking

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AVLSM: Adaptive Variational level set model for Image Segmentation in the Presence of Severe Intensity Inhomogeneity and High Noise

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 43-57页

作者： Cai, Qing Qian, Yiming Zhou, Sanping Li, Jinxing Yang, Yee-Hong Wu, Feng Zhang, David Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Guangdong Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Anhui Peoples R China Univ Manitoba Dept Comp Sci Winnipeg MB R3T 2N2 Canada Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China Harbin Inst Technol Shenzhen 518055 Guangdong Peoples R China Linklogis Shenzhen 518000 Guangdong Peoples R China Univ Alberta Dept Comp Sci Edmonton AB T6G 2E9 Canada Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Shenzhen 518172 Guangdong Peoples R China

Intensity inhomogeneity and noise are two common issues in images but inevitably lead to significant challenges for image segmentation and is particularly pronounced when the two issues simultaneously appear in one image. As a result, most existing level set models yield poor performance when applied to this images. To this end, this paper proposes a novel hybrid level set model, named adaptive variational level set model (AVLSM) by integrating an adaptive scale bias field correction term and a denoising term into one level set framework, which can simultaneously correct the severe inhomogeneous intensity and denoise in segmentation. Specifically, an adaptive scale bias field correction term is first defined to correct the severe inhomogeneous intensity by adaptively adjusting the scale according to the degree of intensity inhomogeneity while segmentation. More importantly, the proposed adaptive scale truncation function in the term is model-agnostic, which can be applied to most off-the-shelf models and improves their performance for image segmentation with severe intensity inhomogeneity. Then, a denoising energy term is constructed based on the variational model, which can remove not only common additive noise but also multiplicative noise often occurred in medical image during segmentation. Finally, by integrating the two proposed energy terms into a variational level set framework, the AVLSM is proposed. The experimental results on synthetic and real images demonstrate the superiority of AVLSM over most state-of-the-art level set models in terms of accuracy, robustness and running time.

关键词： Image segmentation Nonhomogeneous media level set Adaptation models Robustness Estimation TV Image segmentation level set model bias field estimation total variation intensity inhomogeneity correction image denoising

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Automatic Tongue Image Segmentation Based on Thresholding and an Improved level set model 16

Automatic Tongue Image Segmentation Based on Thresholding an...

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16th IEEE Asia Pacific Conference on Circuits and Systems (IEEE APCCAS) / IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (PrimeAsia)

作者： Gu, Hongyu Yang, Zhecheng Chen, Hong Tsinghua Univ Inst Microelect Beijing Peoples R China Natl Lab Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781728193960

Tongue diagnosis is an important and widely used diagnosis method in traditional Chinese medicine. Automatic tongue segmentation is crucial in the digital tongue diagnosis system. In this paper, an automatic tongue segmentation algorithm is proposed, which consists of two steps. In the first step, a thresholding method that combines color and gray level information is designed, which provides the initial contour needed in the second step. Next, an improved level set model based on geodesic active contour and Chan Vese model is put forward for boundary refinement. The weight function of the new model is adapted for a better balance of two sub-models. Experiment results show that the segmented area is more complete and closer to the real target with a lowest average ME value of 0.081 compared with other methods. The robustness of our algorithm is also verified by different tongue images in terms of shapes, lighting conditions and resolutions.

关键词： tongue segmentation thresholding level set model geodesic active contour Chan Vese model

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Saliency-guided level set model for automatic object segmentation

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PATTERN RECOGNITION 2019年 93卷 147-163页

作者： Cai, Qing Liu, Huiying Qian, Yiming Zhou, Sanping Duan, Xiaojun Yang, Yee-Hong Northwestern Polytech Univ Sch Automat Youyi West Rd 127 Xian 710072 Shanxi Peoples R China Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xianning West Rd 28 Xian 710049 Shanxi Peoples R China Northwestern Polytech Univ Natl Key Lab UVA Technol Youyi West Rd 127 Xian 710072 Shanxi Peoples R China

The level set model is a popular method for object segmentation. However, most existing level set models perform poorly in color images since they only use grayscale intensity information to defined their energy functions. To address this shortcoming, in this paper, we propose a new saliency-guided level set model (SLSM), which can automatically segment objects in color images guided by visual saliency. Specifically, we first define a global saliency-guided energy term to extract the color objects approximately. Then, by integrating information from different color channels, we define a novel local multichannel based energy term to extract the color objects in detail. In addition, unlike using a length regularization term in the conventional level set models, we achieve segmentation smoothness by incorporating our SLSM into a graph cuts formulation. More importantly, the proposed SLSM is automatically initialized by saliency detection. Finally, the evaluation on public benchmark databases and our collected database demonstrates that the new SLSM consistently outperforms many state-of-the-art level set models and saliency detecting methods in accuracy and robustness. (C) 2019 Published by Elsevier Ltd.

关键词： level set model Object segmentation Visual saliency Graph cuts Automatic initialization

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An Adaptive Fuzzy level set model With Local Spatial Information for Medical Image Segmentation and Bias Correction

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IEEE ACCESS 2019年 7卷 27322-27338页

作者： Zhang, Zhe Song, Jianhua Heilongjiang Univ Elect Engn Coll Harbin 150080 Heilongjiang Peoples R China Minnan Normal Univ Coll Phys & Informat Engn Zhangzhou 363000 Peoples R China

Medical image segmentation is still a challenging task due to noise and intensity inhomogeneity. An adaptive fuzzy level set model (AFLSM) with local spatial information is presented in this paper for accurately segmenting medical images and correcting bias field. A weighting scheme that can ensure each pixel in the neighborhood to have anisotropic weight is first introduced to remove noisy pixels and hence improve the robustness to noise. Then, a linear combination of orthogonal basis functions is used to represent bias field to ensure its smoothly and slowly varying property. Besides, to improve the robustness to initialization, this adaptive fuzzy level set model fuses a level set model with the membership function of fuzzy clustering, which can adaptively adjust the evolution of level set function. Finally, the distance regularization term in energy formulation is redefined with a novel double-well potential function to inherently maintain the accuracy and stability of the AFLSM. The AFLSM is first represented in the two-phase case and subsequently extended to the multi-phase formulation. The numerous visual segmentation results and quantitative evaluation can demonstrate the performance of the AFLSM on synthetic and real medical images. Comparison with the state-of-the-art models shows that the AFLSM can achieve better segmentation results with an improvement of 0.2286 +/- 0.1477 in Dice coefficient and 0.1350 +/- 0.0661 in Jaccard similarity coefficient in terms of robustness and the capability to correct bias field, respectively.

关键词： Bias field correction image segmentation intensity inhomogeneity level set model medical image

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Automatic Brain Aneurysm Extraction in Angiography Videos using Circlet Transform and a Modified level set model

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CURRENT MEDICAL IMAGING 2018年第6期14卷 923-932页

作者： Momeni, Saba Sarrafzadeh, Omid Rabbani, Hossein Isfahan Univ Med Sci Med Image & Signal Proc Res Ctr Esfahan Iran Islamic Azad Univ Dept Biomed Engn Mashhad Branch Mashhad Iran Isfahan Univ Med Sci Sch Adv Technol Med Esfahan Iran

Background: These days, many attempts have been done to specify the size and location of aneurysms, leading to more successful surgical operation and less bleeding risk. In this paper, a novel method is proposed to extract brain aneurysms from two dimensional x-ray angiography videos, automatically. Methods: The most acute challenges in detecting brain aneurysm are the complexity of vessel structures and shape similarity between the aneurysm and vessel overlaps and vessel cross sections. Therefore, researchers regarded removing vessel structures as an initial and crucial step to detect aneurysm. Since the circularity feature is the most distinctive criteria for physicians to detect aneurysm, firstly, we proposed a robust method based on Fast Circlet Transform (FCT) to localize the aneurysm without needing to remove vessel structures. Then, to segment the detected aneurysm more accurately, a modified level set algorithm is proposed. Finally, our proposed method is quantitatively evaluated on two different datasets with different views, shapes, sizes, locations and contrast. Results & Conclusion: Experimental results show that the proposed system is reliable without dealing with vessel structure removal challenges, reluctant false positive candidates, hard parameter tuning and poor edge gradient.

关键词： Brain aneurysm detection aneurysm segmentation FCT level set model x-ray robust method

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Online SOM-based level set model for image segmentation

Online SOM-based level set model for image segmentation

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2016 4th International Conference on Machinery,Materials and Information Technology Applications(ICMMITA 2016)

作者： Xiaomin Xie College of Mechanical & Electrical Engineering Hohai University

ISBN: (纸本)9781510835467

In this paper, a novel region based active model integrating self organizing map(SOM) for image segmentation is proposed. The SOM is utilized to describe image intensity distributions in an unsupervised way and establish the entire weights set of the neurons. Then, the weights are divided into two branch sets depending on whether the weight is larger than the weight mean. Further we obtain the means of two weights subsets, which could be regarded as the average intensities of the background and foreground, respectively. In addition, the global intensities updated during contour evolution are replaced by the prototypes of the entire weights set according to certain rule. Along with the two weight means, the global means are averaged to create the threshold for the whole image to guide the contour movement. The proposed model could be successfully applied for image segmentation. Experiment results demonstrate the effectiveness of our model.

关键词： SOM level set model Local information Image segmentation

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A level set model for simulating fatigue-driven delamination in composites

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INTERNATIONAL JOURNAL OF FATIGUE 2015年 80卷 434-442页

作者： Latifi, M. van der Meer, F. P. Sluys, L. J. Delft Univ Technol Fac Civil Engn & Geosci NL-2600 GA Delft Netherlands

This paper proposes a level set model for simulating delamination propagation in composites under high-cycle fatigue loading. For quasi-static loading conditions, interface elements with a cohesive law are widely used for the simulation of delamination. However, basic concepts from fatigue analysis such as the notion that the crack growth rate is a function of energy release rate cannot be embedded in existing cohesive laws. Therefore, we propose a model in which the cohesive zone is eliminated from the computation while maintaining the flexibility that the crack shape is not bound to element edges. The model is able to predict the delamination growth rate and its front shape accurately. To demonstrate the validity of the model, several tests under different fracture modes are conducted and the results are compared with experimental data, analytical solutions and results from cohesive zone analysis. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Fatigue Delamination Extended finite element Laminate composites level set model

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An Adrenal Segmentation model Based on Shape Associating level set in Sequence of CT Images

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2019年第10期91卷 1169-1177页

作者： Zhang, Guoying Li, Zhiwei China Univ Min & Technol Beijing CampusDing 11Xueyuan Rd Beijing Peoples R China

The segmentation challenge of adrenal and surrounding tissues lie in the similar CT values and adhesion in a medical image. An adrenal segmentation model (SALS) based on shape associating level set is proposed to segment the adrenal accurately. The objective function of adrenal boundary is expressed by a level set model. The prior shape curve of the adrenal to be segmented is calculated by the gradual change relationship between the adrenal glands in adjacent CT images, and is converted into shape constraint term of the level set models. A 3D Laplace method is used to improve adrenal grey scale information. The improved result of CT image is converted into grey scale constraint term of the level set models. Under the two constraints, the objective function of adrenal boundary converges to the best boundary. The level set methods in the literature obtain a prior shape by training a large number of sample images, and use the shape modes to segment CT images. The SALS model does not depend on the sample images. The adrenal boundaries in sequence of CT images can be directly segmented by SALS model, and the segmented boundaries are more accurate than the traditional level set methods. The SALS model has stronger adaptability to adherent adrenal boundary.

关键词： level set model Prior shape Gradual change relationship 3D Laplace method Sequence of CT images

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Long-term simulation of crystallization fouling in a forced circulation crystallizer

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2024年 231卷

作者： Darand, Jamal Jafarian, Ali Tarbiat Modares Univ Sch Mech Engn POB 14115-143 Tehran Iran

Most heat exchangers in wastewater treatment systems and the thermal desalination industry experience the deposition of unwanted materials on their surfaces. One of the primary causes is the supersaturation of salts with inverse solubility near the heat exchanger's heated surfaces. In this paper, a long-term simulation of the crystallization fouling of calcium carbonate by developing an efficient solver in the OpenFOAM software is proposed. The k-omega SST model was used to simulate turbulence, and the level-set model was adopted for high-precision and high-quality capturing of the fouling front inside the heat transfer tubes of a forced circulation crystallizer. The results demonstrated that the developed solver is a promising tool for accurately predicting moving boundary conjugate heat transfer. Upon validation of the results, the dynamic evolution of the fouling front and its sophisticated interactions with hydrodynamics were examined. The crystallization fouling was found to be under reaction control. Furthermore, the relatively slight supersaturation caused delayed front growth, which prolonged the fouling time to several months. Besides, findings demonstrated that the intensive competition between removal and deposition mechanisms at higher inlet velocities resulted in decreased asymptotic thermal resistance. Additionally, the velocity increase had a smaller impact on reducing the deposit's asymptotic resistance.

关键词： Crystallization fouling Supersaturation Fouling front capturing level set model ZLD technology Forced circulation crystallizer Numerical simulation OpenFOAM open source software

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