scholarly journals COVID-19 Lesion Segmentation Using Lung CT Scan Images: Comparative Study Based on Active Contour Models

2021 ◽  
Vol 11 (17) ◽  
pp. 8039
Author(s):  
Younes Akbari ◽  
Hanadi Hassen ◽  
Somaya Al-Maadeed ◽  
Susu M. Zughaier
Keyword(s):  
Image Segmentation ◽  
Ct Scan ◽  
Active Contour ◽  
State Of The Art ◽  
Age Groups ◽  
Lesion Segmentation ◽  
Active Contour Models ◽  
Contour Models ◽  
Lung Ct ◽  
Lung Ct Scan

Pneumonia is a lung infection that threatens all age groups. In this paper, we use CT scans to investigate the effectiveness of active contour models (ACMs) for segmentation of pneumonia caused by the Coronavirus disease (COVID-19) as one of the successful methods for image segmentation. A comparison has been made between the performances of the state-of-the-art methods performed based on a database of lung CT scan images. This review helps the reader to identify starting points for research in the field of active contour models on COVID-19, which is a high priority for researchers and practitioners. Finally, the experimental results indicate that active contour methods achieve promising results when there are not enough images to use deep learning-based methods as one of the powerful tools for image segmentation.

scholarly journals COVID-19 Lesion Segmentation using Lung CT Scan Images: Comparative Study based on Active Contour Models

2020 ◽  
Author(s):  
Younes Akbari ◽  
Hanadi Hassen ◽  
Somaya Al-maadeed ◽  
Susu M. Zughaier
Keyword(s):  
Ct Scan ◽  
Active Contour ◽  
Age Groups ◽  
Machine Learning Techniques ◽  
Object Boundary ◽  
Active Contour Models ◽  
Advantages And Disadvantages ◽  
Contour Models ◽  
Lung Ct ◽  
Lung Ct Scan

Abstract Pneumonia is a lung infection threaten that threats all age groups. In this paper, using CT scans images, we used active contour models to evaluate and determine pneumonia infection caused by the Coronavirus disease (COVID-19). A background of active contour models (ACM) including contour representation and object boundary description methods is presented. The focus of this paper is on the conducted works based on the external forces. These methods include edge-based and region-based methods. Furthermore, the explanations of these methods, as well as the advantages and disadvantages of each method are presented. Finally, a comparison between the performances of the conducted works has been done based on a database of Lung CT Scan Images. The present review helps readers identify research starting points in active contour models on COVID19 research, which is a high priority topic to guide researchers and practitioners. In addition, when there are not enough images to use machine learning techniques, such as deep learning methods, the experimental results indicate that active contour methods obtain promising results.

T1w MRI Brain Tumor Image Segmentation using Active Contour Models and Convolution Neural Network Fusion

2021 ◽  
Author(s):  
Vamisdhar Entireddy ◽  
Babu K Rajesh ◽  
R Sampathkumar ◽  
Jyothirmai Gandeti ◽  
Syed Shameem ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Active Contour ◽  
Convolution Neural Network ◽  
Active Contour Models ◽  
Mri Brain ◽  
Contour Models

scholarly journals Robust Evolution Method of Active Contour Models and Application in Segmentation of Image Sequence

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Guoqi Liu ◽  
Haifeng Li
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Image Sequence ◽  
Nuclear Norm ◽  
Shape Prior ◽  
Object Boundary ◽  
Active Contour Models ◽  
Ideal Object ◽  
Sequence Segmentation ◽  
Contour Models

Active contour models are widely used in image segmentation. In order to obtain ideal object boundary, researchers utilize various information to define new models for image segmentation. However, the models could not meet all scenes of image. In this paper, we propose a block evolution method to improve the robustness of contour evolution. A block matrix is consisted of contours of former iterations and contours of shape prior, and a nuclear norm of the matrix is a measure of the similarity of these shapes. The constraint of the nuclear norm minimization is imposed on the evolution of active contour models, which could avoid large deformation of the adjacent curves and keep the shape conformability of contour in the evolution. The shape prior can be integrated into the block evolution method, which is effective in dealing with missing features of images and noise. The proposed method can be applied to image sequence segmentation. Experiments demonstrate that the proposed method improves the robust performance of active contour models and can increase the flexibility of applications in image sequence segmentation.

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