A hybrid active contour model based on global and local information for medical image segmentation

2018 ◽  
Vol 30 (2) ◽  
pp. 689-703 ◽  
Author(s):  
Lingling Fang ◽  
Tianshuang Qiu ◽  
Hongyang Zhao ◽  
Fang Lv
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Medical Image Segmentation ◽  
Local Information ◽  
Model Based ◽  
Contour Model ◽  
Global And Local

Active contour model based on local and global Gaussian fitting energy for medical image segmentation

Optik ◽  
2018 ◽  
Vol 158 ◽  
pp. 1160-1169 ◽  
Author(s):  
Wencheng Zhao ◽  
Xianze Xu ◽  
Yanyan Zhu ◽  
Fengqiu Xu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Medical Image Segmentation ◽  
Model Based ◽  
Gaussian Fitting ◽  
Contour Model

scholarly journals Active contour model based on local and global intensity information for medical image segmentation

2016 ◽  
Vol 186 ◽  
pp. 107-118 ◽  
Author(s):  
Sanping Zhou ◽  
Jinjun Wang ◽  
Shun Zhang ◽  
Yudong Liang ◽  
Yihong Gong
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Medical Image Segmentation ◽  
Model Based ◽  
Contour Model ◽  
Intensity Information

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.

Medical image segmentation by combing the local, global enhancement, and active contour model

2019 ◽  
Author(s):  
Viacheslav V. Voronin ◽  
Evgenii Semenishchev ◽  
Marina Pismenskova ◽  
Oxana S. Balabaeva ◽  
Sos S. Agaian
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Medical Image Segmentation ◽  
Contour Model
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