scholarly journals A New Active Contour Medical Image Segmentation Method Based on Fractional Varying-Order Differential

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 206
Author(s):  
Yanshan Zhang ◽  
Yuru Tian
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Similarity Coefficient ◽  
Medical Image Segmentation ◽  
Contour Length ◽  
Dice Similarity Coefficient ◽  
Penalty Term ◽  
P Rate ◽  
Local Term

Image segmentation technology is dedicated to the segmentation of intensity inhomogeneous at present. In this paper, we propose a new method that incorporates fractional varying-order differential and local fitting energy to construct a new variational level set active contour model. The energy functions in this paper mainly include three parts: the local term, the regular term and the penalty term. The local term combined with fractional varying-order differential can obtain more details of the image. The regular term is used to regularize the image contour length. The penalty term is used to keep the evolution curve smooth. True positive (TP) rate, false positive (FP) rate, precision (P) rate, Jaccard similarity coefficient (JSC), and Dice similarity coefficient (DSC) are employed as the comparative measures for the segmentation results. Experimental results for both synthetic and real images show that our method has more accurate segmentation results than other models, and it is robust to intensity inhomogeneous or noises.

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.

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.

Active Contour Model for Medical Applications

2017 ◽  
pp. 655-678
Author(s):  
Ritam Saha ◽  
Mrinal Kanti Bhowmik
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Research Field ◽  
Medical Image Segmentation ◽  
Accurate Information ◽  
Recent Developments ◽  
Long Time ◽  
Capture Range

Recent developments in medical imaging techniques have brought an entirely new research field. Medical images are frequently corrupted by inherent noise and artifacts that could make it difficult to extract accurate information, and hence compromising the quality of clinical examination. So accurate detection is one of the major problems for medical image segmentation. Snakes or Active contour method have gained wide attention in medical image segmentation for a long time. A Snake is an energy-minimizing spline that controlled by an external energy and influenced by image energy that pull it towards features such as lines and edges. One of the key difficulties with traditional active contour algorithms is a large capture range problem. The contribution of this paper is that to in-depth analysis of the existing different contour models and implementation of techniques with minor improvements that to solve the large capture range problem. The experiment results of this model attain high accuracy detection and outperform the classical snake model in terms of efficiency and robustness.

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