- 标题
- 摘要
- 关键词
- 实验方案
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An Image Segmentation Method Based on Improved Regularized Level Set Model
摘要： When the level set algorithm is used to segment an image, the level set function must be initialized periodically to ensure that it remains a signed distance function (SDF). To avoid this defect, an improved regularized level set method-based image segmentation approach is presented. First, a new potential function is defined and introduced to reconstruct a new distance regularization term to solve this issue of periodically initializing the level set function. Second, by combining the distance regularization term with the internal and external energy terms, a new energy functional is developed. Then, the process of the new energy functional evolution is derived by using the calculus of variations and the steepest descent approach, and a partial differential equation is designed. Finally, an improved regularized level set-based image segmentation (IRLS-IS) method is proposed. Numerical experimental results demonstrate that the IRLS-IS method is not only effective and robust to segment noise and intensity-inhomogeneous images but can also analyze complex medical images well.
关键词： image segmentation,energy functional,level set,distance regularization term
更新于2025-09-23 15:23:52
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A methodology for topology optimization based on level set method and its application to piezoelectric energy harvester design
摘要： Compared to a density based procedure, the level set method can acquire a clear boundary without any intermediate density elements, and has become a topical issue in topology optimization studies. In this method, the material properties of the discretized elements are usually defined as the design variables, giving rise to the so called discrete or sawtooth topologies. In this paper, a topology optimization methodology based on level-set method (LSM) and radial basis function (RBF) is proposed. To enhance the performance of the LSM, an improved material interpolation model is proposed. The RBF based post-processor is proposed to smooth the preliminary optimized topology. The proposed method is applied to the topology design of a piezoelectric energy harvester. The effect of different penalization factors is compared and analyzed. The numerical results validate the feasibility and effectiveness of the proposed method.
关键词： Coupled problems,topology optimization,level set method
更新于2025-09-23 15:22:29
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[IEEE 2018 3rd International Conference for Convergence in Technology (I2CT) - Pune (2018.4.6-2018.4.8)] 2018 3rd International Conference for Convergence in Technology (I2CT) - Modified Level-Set for Segmenting Breast Tumor from Thermal Images
摘要： Contactless, painless and radiation-free thermal imaging technique is one of the preferred screening modalities for detection of breast cancer. However, poor signal to noise ratio and the inexorable need to preserve edges defining cancer cells and normal cells, make the segmentation process difficult and hence unsuitable for computer aided diagnosis of breast cancer. This work uses a modified version of level-set called marker-controlled level-set for segmentation along with pre-processing. This paper presents key findings from a research conducted on the appraisal of two promising techniques, for the detection of breast cancer: a) marker-controlled Level-set segmentation of anisotropic diffusion filtered preprocessed image versus b) Segmentation using marker-controlled level-set on a Gaussian-filtered image. The proposed method was carried out on images from an online database with samples collected from women of varying breast characteristics. It was observed that the breast was able to be segmented out from the background by adjustment of the markers. From the results, it was observed that as a pre-processing technique, anisotropic filtering with level-set segmentation, preserved the edges more effectively than Gaussian filtering. Segmented image, by application of anisotropic filtering was found to be more suitable for feature extraction, enabling automated computer aided diagnosis of breast cancer.
关键词： Breast,Thermograms,Gaussian,Anisotropic diffusion,Segment,Level set
更新于2025-09-23 15:22:29
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Automatic optic disc localization and segmentation in retinal images by a line operator and level sets
摘要： BACKGROUND: Existing methods may fail to locate and segment the optic disc (OD) due to imprecise boundaries, inconsistent image contrast and deceptive edge features in retinal images. OBJECTIVE: To locate the OD and detect the OD boundary accurately. METHODS: The method exploits a multi-stage strategy in the detection procedure. Firstly, OD location candidate regions are identi?ed based on high-intensity feature and vessels convergence property. Secondly, a line operator ?lter for circular brightness feature detection is designed to locate the OD accurately on candidates. Thirdly, an initialized contour is obtained by iterative thresholding and ellipse ?tting based on the detected OD position. Finally, a region-based active contour model in a variational level set formulation and ellipse ?tting are employed to estimate the OD boundary. RESULTS: The proposed methodology achieves an accuracy of 98.67% for OD identi?cation and a mean distance to the closest point of 2 pixels in detecting the OD boundary. CONCLUSION: The results illuminate that the proposed method is effective in the fast, automatic, and accurate localization and boundary detection of the OD. The present work contributes to the more effective evaluation of the OD and realizing automatic screening system for early eye diseases to a large extent.
关键词： optic disc segmentation,level set method,retinal images,Optic disc localization
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Main Aortic Segmentation from CTA with Deep Feature Aggregation Network
摘要： In this study, we propose a Deep Feature Aggregation network (DFA-Net) for main aortic segmentation from CTA(Computed Tomography Angiography) by aggregating features from forwarding layers to leverage more visual information. To practically verify the effectiveness of our method, we collect 90 CTA volumes from Beijing AnZhen Hospital up to over 60 thousands 2-D slices. First, we use a level-set based algorithm to efficiently generate the dataset for training and validating the deep model. Then the dataset is divided into three parts, 70 instances are used for training and 5 instances are used for validating the best parameters, and the rest 15 instances are used for testing the generalization of the model. Finally, the testing result shows that mIoU(mean Intersection-over-Union) of the segmentation result is 0.943, which indicates that by properly aggregating more visual features in a deep network the segmentation model can achieve state-of-the-art performance.
关键词： CTA,feature aggregation,level set,main aortic segmentation
更新于2025-09-23 15:22:29
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[ACM Press the 2018 International Conference - Hong Kong, Hong Kong (2018.02.24-2018.02.26)] Proceedings of the 2018 International Conference on Image and Graphics Processing - ICIGP 2018 - The Optimized Level Set Image Segmentation Based on Saliency Maps
摘要： In order to improve the practicability of the level set method and reduce the computational cost, a optimized level set active contour model that embeds the image local information is proposed in this paper. Firstly, the optimal saliency method of the image is selected by comparing three saliency methods, which is helped to generate the initial contour of the image. Secondly, a new variational level set model integrating edge information and regional local information is presented, and a local energy term is added to the energy function. Finally, image segmentation is implemented by new level set methods based on optimal saliency method. Experiments demonstrate the results of the new level set method based on optimal saliency method are higher than the Distance Regularized Level Set Evolution (DRLSE) model in terms of both efficiency and accuracy. Moreover, the segmentation time of the optimization algorithm only needs 1.94% of the former, and it has high segmentation accuracy.
关键词： the level set method,Image segmentation,Saliency map,Distance Regularized Level Set Evolution (DRLSE)
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Understanding hole transport across amorphous Si passivation layers in Si heterojunction solar cells using Monte Carlo simulation
摘要： Deformable models and level set methods have been extensively investigated for computerized image segmentation. However, medical image segmentation is yet one of open challenges owing to diversified physiology, pathology, and imaging modalities. Existing level set methods suffer from some inherent drawbacks in face of noise, ambiguity, and inhomogeneity. It is also refractory to control level set segmentation that is dependent on image content and evolutional strategies. In this paper, a new level set formulation is proposed by using fuzzy region competition for selective image segmentation. It is able to detect and track the arbitrary combination of selected objects or image components. To the best of our knowledge, this new formulation should be one of the first proposals in a framework of region competition for selective segmentation. Experiments on both synthetic and real images validate its advantages in selective level set segmentation.
关键词： image segmentation,region competition,level set methods,Fuzzy control,selective segmentation
更新于2025-09-23 15:19:57
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[IEEE 2018 Asia Communications and Photonics Conference (ACP) - Hangzhou (2018.10.26-2018.10.29)] 2018 Asia Communications and Photonics Conference (ACP) - Tunable Fiber Laser for On-demand Mode in C and L Bands
摘要： Deformable models and level set methods have been extensively investigated for computerized image segmentation. However, medical image segmentation is yet one of open challenges owing to diversified physiology, pathology, and imaging modalities. Existing level set methods suffer from some inherent drawbacks in face of noise, ambiguity, and inhomogeneity. It is also refractory to control level set segmentation that is dependent on image content and evolutional strategies. In this paper, a new level set formulation is proposed by using fuzzy region competition for selective image segmentation. It is able to detect and track the arbitrary combination of selected objects or image components. To the best of our knowledge, this new formulation should be one of the first proposals in a framework of region competition for selective segmentation. Experiments on both synthetic and real images validate its advantages in selective level set segmentation.
关键词： image segmentation,level set methods,Fuzzy control,region competition,selective segmentation
更新于2025-09-23 15:19:57
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[IEEE 2018 7th European Workshop on Visual Information Processing (EUVIP) - Tampere, Finland (2018.11.26-2018.11.28)] 2018 7th European Workshop on Visual Information Processing (EUVIP) - Automatic 3D Detection and Segmentation of Head and Neck Cancer from MRI Data
摘要： A novel algorithm for automatic head and neck 3D tumour segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm pre-processes the MRI data slices to enhance quality and reduce artefacts. An intensity standardisation process is performed between slices, followed by cancer region segmentation of central slice, to get the correct intensity range and rough location of tumour regions. Fourier interpolation is applied to create isotropic 3D MRI volume. A new location-constrained 3D level set method segments the tumour from the interpolated MRI volume. The proposed algorithm is tested on real MRI data. The results show that the novel 3D tumour volume extraction algorithm has an improved dice score and F-measure when compared to the previous 2D and 3D segmentation method.
关键词： fuzzy clustering,magnetic resonance imaging,Fourier interpolation,head and neck cancer,3D level set method
更新于2025-09-19 17:15:36
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Automatic segmentation in image stacks based on multi-constraint level-set evolution
摘要： Contour extraction of image stacks is a basic task in medical modeling. The existing level-set methods usually suffer from some problems (e.g. serious errors around sharp features, incorrect split of topology and contour occlusions). This paper proposes a novel method of multi-constraint level-set evolution to avoid above-mentioned problems. Interpolation constraint and deviation constraint are added to evolution process in addition to existing constraints (such as edge points and areas). In order to prevent occlusions, it proposes a method of three-phase level-set evolution. The first phase obtains a rough contour according to selected edge points. The second phase applies an expanding LSE (level-set evolution). Missing edge points in the first phase are added when occlusions probably appear. In the third phase, occlusions are deleted and a refining evolution is implemented. As proved by final experiments, our method can steadily extract contours slice by slice when the shapes of previous contours (contours in the previous slice) are similar to current contours (contours in the current slice). Furthermore, there is no error propagation during the process of contour extraction.
关键词： level-set evolution,multi-constraint,Contour extraction
更新于2025-09-19 17:15:36