scholarly journals Multi-Feature Driven Active Contour Segmentation Model for Infrared Image With Intensity Inhomogeneity

2021 ◽  
Author(s):  
Qinyan Huang ◽  
Weiwen Zhou ◽  
Minjie Wan ◽  
Xin Chen ◽  
Kan Ren ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Infrared Image ◽  
Weight Coefficient ◽  
Convergence Result ◽  
Intensity Inhomogeneity ◽  
Contour Segmentation ◽  
Feature Information ◽  
Local Range

Abstract Active contour model (ACM) is one of the most widely used image segmentation tools at present, but the existing methods only utilize single feature information of image to minimize the energy function, which is easy to cause false segmentations in infrared (IR) images. In this paper, we propose a multi-feature driven active contour segmentation model to handle IR images with intensity inhomogeneity. Firstly, an especially-designed signed pressure force (SPF) function is constructed by combining the global information calculated by global average gray information and the local multi-feature information calculated by local entropy, local standard deviation and gradient information. Then, we draw upon adaptive weight coefficient computed by local range to incorporate the afore-mentioned global term and local term. Next, the SPF function is substituted into the level set formulation (LSF) for further evolution. Finally, the LSF converges after a finite number of iterations and the IR image segmentation result is obtained from the corresponding convergence result. Experimental results demonstrate that the presented method outperforms typical models in terms of precision rate and overlapping rate in IR test images.

An Improved Image Segmentation Active Contour Model

2014 ◽  
Vol 513-517 ◽  
pp. 3463-3467
Author(s):  
Li Fen Zhou ◽  
Chang Xu Cai
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Local Region ◽  
Intensity Inhomogeneity ◽  
Contour Model ◽  
Global And Local ◽  
Region Information

The Chan-Vese (C-V) active contour model has low computational complexity, initialization and insensitive to noise advantagesand utilizes global region information of images, so it is difficult to handle images with intensity inhomogeneity. The Local binary fitting (LBF) model based on local region information has its certain advantages in mages segmentation of weak boundary or uneven greay.but , the segmentation results are very sensitive to the initial contours, In order to address this problem, this paper proposes a new active contour model with a partial differential equation, which integrates both global and local region information. Experimental results show that it has a distinctive advantage over C-V model for images with intensity inhomogeneity, and it is more efficient than LBF.

scholarly journals Local and Global Active Contour Model for Image Segmentation With Intensity Inhomogeneity

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 54224-54240 ◽  
Author(s):  
Qing Cai ◽  
Huiying Liu ◽  
Yiming Qian ◽  
Jing Li ◽  
Xiaojun Duan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Intensity Inhomogeneity ◽  
Contour Model

Image Segmentation Based on Fractional Differentiation and RSF Model

2017 ◽  
Author(s):  
Guimei Zhang ◽  
Yangang Zhu ◽  
Jianxin Liu ◽  
YangQuan Chen
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Intensity Inhomogeneity ◽  
Fractional Differentiation ◽  
Contour Model ◽  
Texture Region ◽  
Initial Location ◽  
Evolution Curve ◽  
Weak Texture

Intensity inhomogeneity or weak texture region image segmentation plays an important role in computer vision and image processing. RSF (Region-Scalable Fitting) active contour model has been proved to be an effective method to segment intensity inhomogeneity. However RSF model is sensitive to the initial location of evolution curve , it tends to fall into local optimal. Aiming at the problem, this paper proposed a new method for image segmentation based on fractional differentiation and RSF model. The proposed method adds the global Grünwald-Letnikov fractional gradient into the RSF model. Thus the gradient of the intensity inhomogeneity and weak texture regions is strengthened. As a result, both the robustness of initial location of evolution curve and efficiency of image segmentation are improved. Theoretical analysis and experimental results demonstrate that the proposed algorithm is capable of segmenting the intensity inhomogeneities and weak texture images. It is robust to curve initial location, furthermore the efficiency of segmentation is improved.

scholarly journals Multi-feature driven active contour segmentation model for infrared image with intensity inhomogeneity

2021 ◽  
Vol 53 (7) ◽  
Author(s):  
Qinyan Huang ◽  
Weiwen Zhou ◽  
Minjie Wan ◽  
Xin Chen ◽  
Kan Ren ◽  
...  
Keyword(s):  
Active Contour ◽  
Infrared Image ◽  
Intensity Inhomogeneity ◽  
Contour Segmentation

scholarly journals A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Jiao Shi ◽  
Jiaji Wu ◽  
Anand Paul ◽  
Licheng Jiao ◽  
Maoguo Gong
Keyword(s):  
Image Segmentation ◽  
Real World ◽  
Active Contour ◽  
Active Contour Model ◽  
Intensity Inhomogeneity ◽  
Curve Evolution ◽  
Evolution Theory ◽  
Piecewise Constant ◽  
Contour Model ◽  
Proposed Model

Active contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable difficulties in image segmentation. A milder assumption that the image is statistically homogeneous within different local regions may better suit real world images. By taking local image information into consideration, an enhanced active contour model is proposed to overcome difficulties caused by intensity inhomogeneity. In addition, according to curve evolution theory, only the region near contour boundaries is supposed to be evolved in each iteration. We try to detect the regions near contour boundaries adaptively for satisfying the requirement of curve evolution theory. In the proposed method, pixels within a selected region near contour boundaries have the opportunity to be updated in each iteration, which enables the contour to be evolved gradually. Experimental results on synthetic and real world images demonstrate the advantages of the proposed model when dealing with intensity inhomogeneity images.

scholarly journals New Region-Scalable Discriminant and Fitting Energy Functional for Driving Geometric Active Contours in Medical Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Xuchu Wang ◽  
Yanmin Niu ◽  
Liwen Tan ◽  
Shao-Xiang Zhang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Active Contour ◽  
Medical Image ◽  
Active Contours ◽  
Active Contour Model ◽  
Energy Functional ◽  
Medical Image Segmentation ◽  
Intensity Inhomogeneity ◽  
Boundary Problems

We propose a novel region-based geometric active contour model that uses region-scalable discriminant and fitting energy functional for handling the intensity inhomogeneity and weak boundary problems in medical image segmentation. The region-scalable discriminant and fitting energy functional is defined to capture the image intensity characteristics in local and global regions for driving the evolution of active contour. The discriminant term in the model aims at separating background and foreground in scalable regions while the fitting term tends to fit the intensity in these regions. This model is then transformed into a variational level set formulation with a level set regularization term for accurate computation. The new model utilizes intensity information in the local and global regions as much as possible; so it not only handles better intensity inhomogeneity, but also allows more robustness to noise and more flexible initialization in comparison to the original global region and regional-scalable based models. Experimental results for synthetic and real medical image segmentation show the advantages of the proposed method in terms of accuracy and robustness.

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