Segmentation of cardiac tagged MR images using a snake model based on hybrid gradient vector flow

Zhuo, Yu Guo; Xiong, Wei; Zhang, Chengde; Hu, Huaifei

doi:10.1007/s11042-017-5013-2

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Te average degree of overlapping for epicardial segments was 95.88%. Te experimental fndings indicate that this approach efectively enhances the performance of fragmentation than some existing approaches [5]. To address the problem that particle flters cannot obtain accurate hand information, Yi proposes an algorithm combining the flter vector fow Snake model.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Cheng¹,

2023

Mobile Information Systems

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Big data processing includes multiple processing flows of data. But data quality is the most important part of the entire process. Every data processing link will have an impact on the quality of big data. Collision detection is an important research content in many fields such as computer graphics and computer virtual reality. In layman's terms, it means that the computer detects the signal voltage on the channel while sending data. When the signal voltage swing value detected by a station exceeds a certain threshold value, it will be considered that at least two stations on the bus are sending data at the same time, indicating that a collision has occurred. As the number of questions increases, the level becomes higher, the target information becomes more diverse, and the algorithm becomes more complex. The traditional evolutionary algorithm is far from being able to deal with this situation effectively, and the optimization target algorithm emerges as the times require. Evolutionary computing is a mature global optimization method with high robustness and wide applicability. It has the characteristics of self-organization, self-adaptation, and self-learning. It is not limited by the nature of the problem and can effectively deal with complex problems that are difficult to solve by traditional optimization algorithms. This paper aims to study the optimization algorithm of real-time collision detection based on Snake model in the field of big data. It is expected that, with the support of big data technology, the efficiency of real-time collision detection of Snake model will be improved and time will be saved. This paper proposes a multiline swarm particle swarm algorithm and combines it with the Snake model to improve the detection efficiency of the collision detection algorithm. It verifies the detection performance of traditional algorithms and tests their effectiveness in detection. The experimental results of this paper show that the frame rate of the Snake model algorithm is 15, the frame rate of the K-DOPs algorithm is 6.7, and the error of the algorithm is 1.04. It shows that the frame rate of Snake model algorithm is better.

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Section: Related Workmentioning

confidence: 99%

Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Cheng¹,

2023

Mobile Information Systems

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“…The models and texture for segmentation were presented by Gaussian mixture models [4]. Then, the development of this model in bandelet for pancreas segmentation is proposed in [22]. In these ways, Gaussian mixture models applied in texture features or transform for classification and medical image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Segmentation based on Fully Convolutional Network and Minimizing Energy Between Curves

Tuyet¹,

Binh²

2020

TEM Journal

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Energy between curves of image has useful for object contour. The edge map is an important task for recognition. The shape that is found by linking between edges will clearly present the useful information of objects. The aim of medical image segmentation is the representation of a medical image into small pieces. In this process, feature extraction must adapt with edge map completely. This paper proposed a solution for medical image segmentation based on fully convolutional network with gradient vector flow snake in bandelet domain. Our approach depends on decomposition in bandelet domain and reconstruction in contour detection by fully convolutional network combining with gradient vector flow snake. To improve the accuracy of the feature's extraction processing, the proposed method detected the edge map in bandelet domain by using fully convolutional network. And its reconstructed objects contour by using gradient vector flow snake combined with the boundary condition. The results of the proposed method have the segmentation clearly with small details of medical images in high-quality and low-quality cases.

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“…However, several major defects are linked with the level set methods. The edge-based approach [17]- [19], methods' initial contour position will affect the final segmentation result, unable to realize the adaptive segmentation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Adaptive Level Set Segmentation Method Based on Saliency

et al. 2019

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Image segmentation is an important research of computer vision. Due to the effects of intensity inhomogeneity, target edge and background complex, it is still challenging to achieve effective segmentation of target adaptively. To solve these issues, an image segmentation method based on saliency and level set is proposed in this paper. First, adaptive initial contour of level set is got by wavelet-based feature probability evaluation (WFPE) model, the initial contour is closer to the target contour, which can reduce background interference and evolve faster. Second, in order to realize the best detection of intensity mutation and locate the target edge more accurately, an edge constraint energy term is introduced with multiscale information obtained by wavelet transform. Finally, to improve segmentation adaptability and speed, the region information and edge constraint energy term are merged into the adaptive active contour model, the final evolution curve evolves in coarse scale, and then interpolates to get the final segmentation contour. Experimental results show that the proposed method achieves high efficiency in the following aspects: adaptability to images, speed of evolution, close to human visual perception.

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Segmentation of cardiac tagged MR images using a snake model based on hybrid gradient vector flow

Cited by 6 publications

References 20 publications

Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Medical Image Segmentation based on Fully Convolutional Network and Minimizing Energy Between Curves

Multi-Scale Adaptive Level Set Segmentation Method Based on Saliency

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