scholarly journals An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

2018 ◽  
Vol 8 (12) ◽  
pp. 2576 ◽  
Author(s):  
Lin Sun ◽  
Xinchao Meng ◽  
Jiucheng Xu ◽  
Yun Tian
Keyword(s):  
Image Segmentation ◽  
Weight Function ◽  
Active Contour ◽  
Active Contour Model ◽  
Mean Value ◽  
Segmentation Method ◽  
Image Information ◽  
Contour Model ◽  
Global Image Information ◽  
Local Image

Inhomogeneous images cannot be segmented quickly or accurately using local or global image information. To solve this problem, an image segmentation method using a novel active contour model that is based on an improved signed pressure force (SPF) function and a local image fitting (LIF) model is proposed in this paper, which is based on local and global image information. First, a weight function of the global grayscale means of the inside and outside of a contour curve is presented by combining the internal gray mean value with the external gray mean value, based on which a new SPF function is defined. The SPF function can segment blurred images and weak gradient images. Then, the LIF model is introduced by using local image information to segment intensity-inhomogeneous images. Subsequently, a weight function is established based on the local and global image information, and then the weight function is used to adjust the weights between the local information term and the global information term. Thus, a novel active contour model is presented, and an improved SPF- and LIF-based image segmentation (SPFLIF-IS) algorithm is developed based on that model. Experimental results show that the proposed method not only exhibits high robustness to the initial contour and noise but also effectively segments multiobjective images and images with intensity inhomogeneity and can analyze real images well.

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>

An Image Segmentation Method of Underwater Targets Based on Active Contour Model

2014 ◽  
Vol 511-512 ◽  
pp. 457-461
Author(s):  
Tao Liu ◽  
Lei Wan ◽  
Xing Wei Liang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Segmentation Method ◽  
Contour Model ◽  
Underwater Targets ◽  
Underwater Target ◽  
Images Processing

The underwater images are disturbed with low signal to noise ratio and edge blur, because there are the light scattering and absorption effects. If the traditional thresholding method is used directly to segment underwater images, it will usually lead to be less effective to process underwater images. An image segmentation method of underwater target based on active contour model was proposed in this paper. Firstly, using Canny edge detection algorithm to detect the edges of the original image to obtain the information of a crude outline, then the algorithm based on C-V active contour model to segment underwater target images was addressed. The images processing results based on threshold segmentation method and C-V model method were compared. Experiments demonstrate the effectiveness of the proposed algorithm for underwater targets images segmentation.

A New Image Segmentation Model

2014 ◽  
Vol 519-520 ◽  
pp. 541-547
Author(s):  
Chao Liu ◽  
Jing Liu ◽  
Lu Lu Zhang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Weighted Average ◽  
Mean Value ◽  
Local Information ◽  
Experimental Result ◽  
New Model ◽  
Contour Model ◽  
Segmentation Accuracy

To build a new image segmentation model based on level set theory : Add edge detection operator to edgeless active contour model to detect local information; introduce adaptive coefficient of area item to let the model autonomously adjust and evolve curve position according to image information; adopt weighted average gray value to replace traditional absolute mean value to reduce error and improve segmentation result. Experimental result comparison shows that the new model can detect global information and local information at the same time, adaptively adjust curve evolution direction, and has a fast segmentation speed. Compared to edgeless active contour model, the new model is a more effective image segmentation method as it has greater advantages in image segmentation accuracy and computational complexity.

A Novel Active Contour Model for Image Segmentation and Bias Correction Using Guided Image Filtering Regularization Term

2017 ◽  
Vol 31 (07) ◽  
pp. 1754013 ◽  
Author(s):  
Haijun Wang ◽  
Ming Liu
Keyword(s):  
Image Segmentation ◽  
Bias Correction ◽  
Active Contour ◽  
Spatial Information ◽  
Active Contour Model ◽  
Image Filtering ◽  
Distribution Fitting ◽  
Contour Model ◽  
Guided Image Filtering ◽  
Local Image

This paper presents a novel active contour model for image segmentation and bias correction in terms of robustness to initialization and intensity inhomogeneity. In our model, the local image intensities are described by Gaussian distributions with different means and variances. The local Gaussian distribution fitting energy with a new guided image filtering (GIF) regularization is proposed. The new guided image regularization not only considers the spatial information, but also utilizes the local image content. So compared with the traditional algorithms, the proposed model is less sensitive to initialization and converges faster. Comparative experiments show the advantage of the proposed method.

scholarly journals Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
I. Cruz-Aceves ◽  
J. G. Avina-Cervantes ◽  
J. M. Lopez-Hernandez ◽  
H. Rostro-Gonzalez ◽  
C. H. Garcia-Capulin ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Active Contour ◽  
Medical Image ◽  
Active Contours ◽  
Active Contour Model ◽  
Magnetic Resonance Images ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
Contour Model

This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation.

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