Initial Contour Automatic Selection of Geometric Active Contour Model

2009 ◽  
Author(s):  
Hongshe Dang ◽  
Ying Hong ◽  
Xin Fang ◽  
Feili Qiang
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Initial Contour ◽  
Contour Model ◽  
Automatic Selection ◽  
Geometric Active Contour ◽  
Selection Of

Sonar Image Segmentation Based on an Improved Selection of Initial Contour of Active Contour Model

2014 ◽  
Vol 709 ◽  
pp. 447-450 ◽  
Author(s):  
Jun Peng Wu ◽  
Hai Tao Guo
Keyword(s):  
Image Segmentation ◽  
Standard Deviation ◽  
Active Contour ◽  
Active Contour Model ◽  
Threshold Value ◽  
Initial Position ◽  
Initial Contour ◽  
Sonar Image ◽  
Contour Model ◽  
Selection Of

The correct sonar image segmentation is an important foundation for underwater target recognition. Because the contour convergence of the active contour model depends on the selection of initial position, the active contour model is applied in sonar image segmentation. This paper proposed a selection method based on local standard deviation of image as the outline of initial contour. Due to the disturbance of noise, sonar image is usually affected in resolution and contrast. Firstly, sonar image is enhanced by top-hat and bottom-hat transformation in image morphology. Then after image enhancement, a suitable threshold value is chose for rough binarization and the standard deviation of target areas to calculate the local image. According to the size of standard deviation of different regions to determine the scope of the initial contour, sonar image segmentation is achieved by active contour algorithm.

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.

A geometric active contour model without re-initialization for color images

2009 ◽  
Vol 27 (9) ◽  
pp. 1411-1417 ◽  
Author(s):  
Ying Zheng ◽  
Guangyao Li ◽  
Xiehua Sun ◽  
Xinmin Zhou
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Color Images ◽  
Contour Model ◽  
Geometric Active Contour

scholarly journals Active contour model for satellitale image combining local and global region based approach

2019 ◽  
Vol 15 (1) ◽  
pp. 247
Author(s):  
Mouri Hayat ◽  
Fizazi Hadria
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Initial Contour ◽  
Multiple Objects ◽  
Image Information ◽  
Contour Model ◽  
Global Image Information ◽  
Segmentation Of Images ◽  
Global And Local ◽  
Local Image

<p>Global and local image information is crucial for accurate segmentation of images with intensity inhomogeneity valuable minute details and multiple objects with various intensities. We propose a region-based active contour model which is able to utilize together local and global image information. The major contribution of this paper is to expand the LIF model which is includes only local image infofmation to a local and global model. The introduction of a new local and global signed pressure force function enables the extraction of accurate local and global image information and extracts multiple objects with several intensities. Several tests performed on some synthetic and real images indicate that our model is effective as well as less sensitivity to the initial contour location and less time compared with the related works. </p><p><em> </em></p>

Heuristic Initialization for Active Contour Models in CT/MRI Image Processing

2015 ◽  
Vol 15 (03) ◽  
pp. 1550010
Author(s):  
Hao Liu ◽  
Hongbo Qian ◽  
Ning Dai ◽  
Jianning Zhao
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Heuristic Method ◽  
Initial Contour ◽  
Active Contour Models ◽  
Contour Model ◽  
Segmentation Approach ◽  
Edge Points ◽  
Contour Models ◽  
Level Set Evolution

It is an important segmentation approach of CT/MRI images to automatically extract contours in every slice using active contour models. The key point of the segmentation approach is to automatically construct initial contours for active contour models because any active contour model is sensitive to its initial contour. This paper presents an algorithm to construct such initial contours using a heuristic method. Assume that the contour in previous slice (previous contour) is accurate. The contour in the current slice (current contour) is constructed according to the previous contour using the way: Recognition and link of edge points of tissues according to the previous contour. The contour linking edge points is used as the initial contour of the distance regularized level set evolution (DRLSE) method and then an accurate contour can be extracted in the current slice.

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