An Improved Region Based Active Contour model for Medical Image Segmentation

2015 ◽  
Vol 8 (1) ◽  
pp. 115-124 ◽  
Author(s):  
Seema Chack ◽  
Poonam Sharma
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Medical Image Segmentation ◽  
Contour Model

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.

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

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

scholarly journals Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Guodong Wang ◽  
Jie Xu ◽  
Qian Dong ◽  
Zhenkuan Pan
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Higher Order ◽  
Medical Image Segmentation ◽  
Diffusion Method ◽  
Split Bregman ◽  
Contour Model ◽  
The Difference

Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities.

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