Analysis of the Critical Point of the Gradient Vector Flow Snake Model

2006 ◽  
Vol 17 (9) ◽  
pp. 1915 ◽  
Author(s):  
Yuan-Quan WANG
Keyword(s):  
Critical Point ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Snake Model

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

2017 ◽  
Vol 77 (17) ◽  
pp. 21879-21904 ◽  
Author(s):  
Zhuo Yu ◽  
Qian Wang ◽  
Wei Xiong ◽  
Chengde Zhang ◽  
Huaifei Hu
Keyword(s):  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Mr Images ◽  
Snake Model ◽  
Model Based ◽  
Tagged Mr Images

Harmonic gradient vector flow external force for snake model

2008 ◽  
Vol 44 (2) ◽  
pp. 105 ◽  
Author(s):  
Y. Wang ◽  
Y. Jia ◽  
L. Liu
Keyword(s):  
External Force ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Snake Model

scholarly journals Object-Oriented Building Contour Optimization Methodology for Image Classification Results via Generalized Gradient Vector Flow Snake Model

2021 ◽  
Vol 13 (12) ◽  
pp. 2406
Author(s):  
Jingxin Chang ◽  
Xianjun Gao ◽  
Yuanwei Yang ◽  
Nan Wang
Keyword(s):  
Image Classification ◽  
Object Oriented ◽  
Initial Contour ◽  
Building Extraction ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Generalized Gradient ◽  
Snake Model ◽  
Line Segments ◽  
Boundary Optimization

Building boundary optimization is an essential post-process step for building extraction (by image classification). However, current boundary optimization methods through smoothing or line fitting principles are unable to optimize complex buildings. In response to this limitation, this paper proposes an object-oriented building contour optimization method via an improved generalized gradient vector flow (GGVF) snake model and based on the initial building contour results obtained by a classification method. First, to reduce interference from the adjacent non-building object, each building object is clipped via their extended minimum bounding rectangles (MBR). Second, an adaptive threshold Canny edge detection is applied to each building image to detect the edges, and the progressive probabilistic Hough transform (PPHT) is applied to the edge result to extract the line segments. For those cases with missing or wrong line segments in some edges, a hierarchical line segments reconstruction method is designed to obtain complete contour constraint segments. Third, accurate contour constraint segments for the GGVF snake model are designed to quickly find the target contour. With the help of the initial contour and constraint edge map for GGVF, a GGVF force field computation is executed, and the related optimization principle can be applied to complex buildings. Experimental results validate the robustness and effectiveness of the proposed method, whose contour optimization has higher accuracy and comprehensive value compared with that of the reference methods. This method can be used for effective post-processing to strengthen the accuracy of building extraction results.

scholarly journals Adoption of Snake Variable Model-Based Method in Segmentation and Quantitative Calculation of Cardiac Ultrasound Medical Images

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xing Huang ◽  
Haozhi Zhu ◽  
Jiexin Wang
Keyword(s):  
Image Segmentation ◽  
Three Dimensional ◽  
Cardiac Ultrasound ◽  
Segmentation Result ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Variable Model ◽  
Snake Model ◽  
Ultrasonic Image ◽  
Quantitative Calculation

This paper intends to explore the effect of the enhanced snake variable model in the segmentation of cardiac ultrasound images and its adoption in quantitative measurement of cardiac cavity. First, the basic principles of the traditional snake model and the gradient vector flow (GVF) snake model are explained. Then, an ellipsoid model is constructed to obtain the initial contour of the heart based on the three-dimensional volume of cardiac ultrasound image, and a discretized triangular mesh model is generated. Finally, the vortical gradient vector flow (VGVF) external force field is introduced and combined with the greedy algorithm to process the deformation of the initial ellipsoid contour of the heart. The segmentation effect is quantitatively evaluated regarding the area overlap rate (AOR) and the mean contour distance (MCD). The results show that the VGVF snake model can segment the deep recessed area of the “U-shaped map” in contrast to the traditional snake model and the GVF snake model. After being applied to ultrasonic image segmentation, the VGVF snake model obtains the segmentation result that is close to the doctor’s manual segmentation result, and the average AOR and MCD are 97.4% and 3.2, respectively. The quantitative evaluation of the cardiac cavity is carried out based on the segmentation results, and the measurement of the volume change of the left ventricle within a cardiac cycle is realized. To sum up, VGVF snake model is superior to the traditional snake and GVF snake models in terms of ultrasonic image segmentation, which realizes the three-dimensional segmentation and quantitative calculation of the cardiac cavity.

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