EFFICIENT CONTOUR DETECTION BASED ON IMPROVED SNAKE MODEL

2004 ◽  
Vol 18 (02) ◽  
pp. 197-209 ◽  
Author(s):  
FRANK Y. SHIH ◽  
KAI ZHANG
Keyword(s):  
Force Field ◽  
Active Contour ◽  
Active Contour Model ◽  
Contour Detection ◽  
Initial Contour ◽  
Object Boundary ◽  
Edge Preserving ◽  
Snake Model ◽  
External Force Field ◽  
Gravitation Force

Active contour model, also called snake, adapts to edges in an image. A snake is defined as an energy minimizing spline – the snake's energy depends on its shape and location within the image. Problems associated with initialization and poor convergence to boundary concavities, however, have limited its utility. In this paper, we present a new external force field, named gravitation force field, for the snake model. We associate this force field with edge preserving smoothing to drive the snake for solving the problems. Our gravitation force field uses gradient values as particles to construct force field in the whole image. This force field will attract the active contour toward the edge boundary. The locations of the initial contour are very flexible, such that they can be very far away from the objects and can be inside, outside, or the mixture. The improved snake can converge toward the object boundary in a fast pace.

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.

NGVF: An improved external force field for active contour model

2007 ◽  
Vol 28 (1) ◽  
pp. 58-63 ◽  
Author(s):  
Ning Jifeng ◽  
Wu Chengke ◽  
Liu Shigang ◽  
Yang Shuqin
Keyword(s):  
Force Field ◽  
External Force ◽  
Active Contour ◽  
Active Contour Model ◽  
External Force Field ◽  
Contour Model

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.

Semi-Automatic Liver Segmentation Using Improved GVF Snake Model

2010 ◽  
Vol 121-122 ◽  
pp. 435-440 ◽  
Author(s):  
Hui Yan Jiang ◽  
Xi He Gao
Keyword(s):  
Force Field ◽  
External Force ◽  
Left Lobe ◽  
Initial Contour ◽  
Ct Image ◽  
Liver Segmentation ◽  
Threshold Method ◽  
Abdominal Ct ◽  
Snake Model ◽  
External Force Field

Snakes are extensively used in computer vision and Image processing. However, when it comes to the liver segmentation from computed tomography (CT) image, the application of the models is limited because it can not extend to certain boundary indentations of the liver. In order to solve this problem, we developed an improved GVF snake model by adding an external force field which can efficiently attract the initial contour to these depression areas, such as the top of the left lobe of liver. The proposed method includes two steps. Firstly, combined with the threshold method and the morphology operation, our model can acquire the initial contour of the liver. Secondly, we create an imposed external force field through the interaction with the system, and we make the initial contour converge under the influence of both GVF field and imposed external force field to get the accurate contour of the liver. The application of this method on abdominal CT image is demonstrated, both qualitatively and quantitatively.

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.

scholarly journals Active contour model for satellitale image combining local and global region based approach

2019 ◽  
Vol 15 (1) ◽  
pp. 247
Author(s):  
Mouri Hayat ◽  
Fizazi Hadria
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Initial Contour ◽  
Multiple Objects ◽  
Image Information ◽  
Contour Model ◽  
Global Image Information ◽  
Segmentation Of Images ◽  
Global And Local ◽  
Local Image

<p>Global and local image information is crucial for accurate segmentation of images with intensity inhomogeneity valuable minute details and multiple objects with various intensities. We propose a region-based active contour model which is able to utilize together local and global image information. The major contribution of this paper is to expand the LIF model which is includes only local image infofmation to a local and global model. The introduction of a new local and global signed pressure force function enables the extraction of accurate local and global image information and extracts multiple objects with several intensities. Several tests performed on some synthetic and real images indicate that our model is effective as well as less sensitivity to the initial contour location and less time compared with the related works. </p><p><em> </em></p>

scholarly journals UNSUPERVISED AUTOMATIC BUILDING EXTRACTION USING ACTIVE CONTOUR MODEL ON UNREGISTERED OPTICAL IMAGERY AND AIRBORNE LIDAR DATA

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 181-188 ◽  
Author(s):  
T. H. Nguyen ◽  
S. Daniel ◽  
D. Guériot ◽  
C. Sintès ◽  
J.-M. Le Caillec
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Airborne Lidar ◽  
Training Data ◽  
Building Extraction ◽  
Snake Model ◽  
Urban Scenes ◽  
Contour Model ◽  
Optical Imagery ◽  
Airborne Lidar Data

<p><strong>Abstract.</strong> Automatic extraction of buildings in urban scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly with the emergence of LiDAR systems since mid-1990s. However, in reality, this task is still very challenging due to the complexity of building size and shape, as well as its surrounding environment. Active contour model, colloquially called snake model, which has been extensively used in many applications in computer vision and image processing, has also been applied to extract buildings from aerial/satellite imagery. Motivated by the limitations of existing snake models dedicated to the building extraction, this paper presents an unsupervised and automatic snake model to extract buildings using optical imagery and an unregistered airborne LiDAR dataset, without manual initial points or training data. The proposed method is shown to be capable of extracting buildings with varying color from complex environments, and yielding high overall accuracy.</p>

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