A New Active Contour Model: Curvature Gradient Vector Flow

2006 ◽  
pp. 633-642 ◽  
Author(s):  
Jifeng Ning ◽  
Chengke Wu ◽  
Shigang Liu ◽  
Peizhi Wen
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Contour Model ◽  
Curvature Gradient

Active Contour Model with Edge Flow Vector for Texture Segmentation

2015 ◽  
Vol 781 ◽  
pp. 511-514
Author(s):  
Tanunchai Boonnuk ◽  
Sanun Srisuk ◽  
Thanwa Sripramong
Keyword(s):  
Medical Imaging ◽  
Active Contour ◽  
Active Contour Model ◽  
Texture Segmentation ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Texture Image ◽  
Contour Model ◽  
Flow Vector ◽  
Edge Flow

In this paper, we propose effective method for texture segmentation using active contour model with edge flow vector. This technique was applied from previous active contour model that uses gradient vector flow as external force. It was observed that our method provided better results for texture segmentation while a traditional active contour model and active contour model with gradient vector flow were not capable to be used with texture image. Thus, texture image such as medical imaging can be identified using active contour model with edge flow vector.

scholarly journals Improved Algorithm for Gradient Vector Flow Based Active Contour Model Using Global and Local Information

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jianhui Zhao ◽  
Bingyu Chen ◽  
Mingui Sun ◽  
Wenyan Jia ◽  
Zhiyong Yuan
Keyword(s):  
Force Field ◽  
External Force ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Computational Cost ◽  
Local Information ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
External Force Field

Active contour models are used to extract object boundary from digital image, but there is poor convergence for the targets with deep concavities. We proposed an improved approach based on existing gradient vector flow methods. Main contributions of this paper are a new algorithm to determine the false part of active contour with higher accuracy from the global force of gradient vector flow and a new algorithm to update the external force field together with the local information of magnetostatic force. Our method has a semidynamic external force field, which is adjusted only when the false active contour exists. Thus, active contours have more chances to approximate the complex boundary, while the computational cost is limited effectively. The new algorithm is tested on irregular shapes and then on real images such as MRI and ultrasound medical data. Experimental results illustrate the efficiency of our method, and the computational complexity is also analyzed.

The Concave Contour Detection Based on GVFsnake

2010 ◽  
Vol 108-111 ◽  
pp. 1296-1301
Author(s):  
Jie Cao ◽  
Xiao Jun Liu ◽  
Zong Li Liu
Keyword(s):  
Active Contour ◽  
Field Experiments ◽  
Active Contour Model ◽  
Research Field ◽  
Contour Detection ◽  
Gradient Vector Flow ◽  
External Energy ◽  
Edge Information ◽  
Snake Model ◽  
Contour Model

Active contour model is an important research field in computer vision and many researchers studied the variational method in recent years. The traditional snake model is unable to converge to the concave area and it has a lower convergence. By improving the external energy, researchers introduced a gradient vector flow active contour model (GVFsnake). Several standard images are used to segmenting experiments, and the results show that GVF has obvious advantages compared with traditional snake model in the iteration number of force field. Experiments show that the method is faster and better to converge in the concave area. The edge information can be kept well and diffused more quickly.

scholarly journals Active Contour Model with Adaptive weighted function for Robust Image Segmentation under Biased Conditions

2021 ◽  
pp. 114811
Author(s):  
Aditi Joshi ◽  
Mohammed Saquib Khan ◽  
Asim Niaz ◽  
Farhan Akram ◽  
Hyun Chul Song ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Weighted Function ◽  
Contour Model ◽  
Robust Image

Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

2021 ◽  
pp. 1-19
Author(s):  
Maria Tamoor ◽  
Irfan Younas
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Heart Diseases ◽  
Medical Image Segmentation ◽  
Gaussian Kernel ◽  
Initial Contour ◽  
Automated Method ◽  
Contour Model

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

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