scholarly journals Some numerical methods for solving geodesic active contour model on image segmentation process

2017 ◽  
Vol 13 (4-1) ◽  
pp. 408-411
Author(s):  
Maizatul Nadirah Mustaffa ◽  
Norma Alias ◽  
Faridah Mustapha
Keyword(s):  
Image Segmentation ◽  
Numerical Methods ◽  
Active Contour ◽  
Active Contour Model ◽  
Central Difference ◽  
Geodesic Active Contour ◽  
Contour Model ◽  
Proposed Model ◽  
Difference Formula ◽  
Edge Based

In this paper, we present an edge-based image segmentation technique using modified geodesic active contour model to detect the desired objects from an image. The stopping function of the proposed model has been modified from the usual geodesic active contour model. The modified geodesic active contour model is discretized using finite difference method based on the central difference formula. Then, some numerical methods such as RBGS and Jacobi methods are used for solving the linear system of equation. The accuracy and effectiveness of the proposed algorithm have been illustrated by applied to different images and some numerical methods.

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 3-D Segmentation of Lung Nodules Using Hybrid Levelsets

2019 ◽  
Author(s):  
Hina Shakir
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Lung Nodules ◽  
Geodesic Active Contour ◽  
3D Slicer ◽  
Anisotropic Diffusion Filter ◽  
Contour Model ◽  
Proposed Model

Lung nodule segmentation in CT images and its subsequent volume analysis can help determinethe malignancy status of a lung nodule. While several efficient segmentation schemes have beenproposed, only a few studies evaluated the segmentation’s performance for large nodules. In thisresearch, we contribute a semi-automatic system which is capable of performing robust 3-D segmen-tations on both small and large nodules with good accuracy. The target CT volume is de-noisedwith an anisotropic diffusion filter and a region of interest is selected around the target nodule ona reference slice. The proposed model performs nodule segmentation by incorporating a mean in-tensity based threshold in Geodesic Active Contour model in level sets. We also devise an adaptivetechnique using image intensity histogram to estimate the desired mean intensity of the nodule.The proposed system is validated on both lung nodules and phantoms collected from publicly avail-able diverse databases. Quantitative and visual comparative analysis of the proposed work withthe Chan-Vese algorithm and statistic active contour model of 3D Slicer platform is also presented.The resulting mean spatial overlap between segmented nodules and reference nodules is 0.855, themean volume bias is 0.10±0.2 ml and the algorithm repeatability is 0.060 ml. The achieved resultssuggest that the proposed method can be used for volume estimations of small as well as large-sizednodules.

scholarly journals Image Segmentation of Thyroid SPECT Using Edge-Based Active Contour Model

2020 ◽  
Vol 1505 ◽  
pp. 012049
Author(s):  
Arif Rahmandinof ◽  
Fadil Nazir ◽  
Yanurita Dwihapsari
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Contour Model ◽  
Edge Based

IMAGE SEGMENTATION WITH GVF FLOW AND DIRECTIONAL INFORMATION

2012 ◽  
Vol 10 (03) ◽  
pp. 1250024
Author(s):  
ZHONGHUA LUO ◽  
JITAO WU
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Intensity Inhomogeneity ◽  
Initial Curve ◽  
Directional Information ◽  
Contour Model ◽  
Model Combining ◽  
Proposed Model ◽  
Edge Location

Intensity inhomogeneity is a common phenomenon in real-world images and may cause many difficulties in image segmentation. To overcome these difficulties, we propose a new active contour model combining the GVF flow and the directional information about edge location. On one hand, we incorporate the GVF flow into the proposed model to segment the images with intensity inhomogeneity efficiently. On the other hand, we construct an alignment term with the directional information to achieve sub-pixel accuracy and relax the placement of the initial curve. Moreover, a regularization term is also included in our model to ensure accurate computation and avoid time-consuming re-initializations. Experimental results on several synthetic and real images show that the proposed model is effective.

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