A hybrid segmentation method based on Gaussian kernel fuzzy clustering and region based active contour model for ultrasound medical images

2015 ◽  
Vol 16 ◽  
pp. 98-112 ◽  
Author(s):  
Deep Gupta ◽  
R.S. Anand ◽  
Barjeev Tyagi
Keyword(s):  
Fuzzy Clustering ◽  
Active Contour ◽  
Active Contour Model ◽  
Medical Images ◽  
Gaussian Kernel ◽  
Segmentation Method ◽  
Contour Model ◽  
Hybrid Segmentation

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.

An Image Segmentation Method of Underwater Targets Based on Active Contour Model

2014 ◽  
Vol 511-512 ◽  
pp. 457-461
Author(s):  
Tao Liu ◽  
Lei Wan ◽  
Xing Wei Liang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Segmentation Method ◽  
Contour Model ◽  
Underwater Targets ◽  
Underwater Target ◽  
Images Processing

The underwater images are disturbed with low signal to noise ratio and edge blur, because there are the light scattering and absorption effects. If the traditional thresholding method is used directly to segment underwater images, it will usually lead to be less effective to process underwater images. An image segmentation method of underwater target based on active contour model was proposed in this paper. Firstly, using Canny edge detection algorithm to detect the edges of the original image to obtain the information of a crude outline, then the algorithm based on C-V active contour model to segment underwater target images was addressed. The images processing results based on threshold segmentation method and C-V model method were compared. Experiments demonstrate the effectiveness of the proposed algorithm for underwater targets images segmentation.

scholarly journals Bone fracture detection using active contour model with prior shape

2021 ◽  
Author(s):  
Yun Jia
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Morphological Operations ◽  
Segmentation Method ◽  
Fracture Detection ◽  
Cast Material ◽  
X Ray ◽  
Detection Tool ◽  
Contour Model ◽  
Prior Shape

In this research, an image segmentation method based on active contouring model was studied, which incorporates the prior shape into the active contour evolving process as the global constraint. The active contour model is implemented based on the level set method. The prior shape regulates the behavior of the active contour and keeps it from leaking out of the weak edges. The goal of this research is to determine the displacement and alignment between two fractured pieces of a bone which is encased in the cast material by segmenting them out and calculating their axes difference. The noise introduced by the cast material makes this task difficult. Morphological operations of dilation and erosion are deployed in this research as the noise reduction and edge detection tool. Experiment results are obtained successfully by applying this method upon the X-ray images of patients' fractured arm.

An active contour model for medical image segmentation using a quaternion framework

2020 ◽  
Vol 2020 (10) ◽  
pp. 62-1-62-6
Author(s):  
V. Voronin ◽  
M. Zhdanova ◽  
E. Semenishchev ◽  
A. Zelensky ◽  
S. Agaian
Keyword(s):  
Active Contour ◽  
Color Image ◽  
Active Contour Model ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Local Polynomial ◽  
Current State ◽  
Contour Model ◽  
Hamiltonian Quaternions ◽  
Algebra Method

This paper presents a new method for segmenting medical images is based on Hamiltonian quaternions and the associative algebra, method of the active contour model and LPA-ICI (local polynomial approximation - the intersection of confidence intervals) anisotropic gradient. Since for segmentation tasks, the image is usually converted to grayscale, this leads to the loss of important information about color, saturation, and other important information associated color. To solve this problem, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. As a method of noise reduction, adaptive filtering based on local polynomial estimates using the ICI rule is used. The presented new approach allows obtaining clearer and more detailed boundaries of objects of interest. The experiments performed on real medical images (Z-line detection) show that our segmentation method of more efficient compared with the current state-of-art methods.

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