scholarly journals General Coastline Extraction Based on an Improved Active Contour Model in Jiaozhou Bay, Qingdao, China, from 1990 to 2018 Using Landsat Satellite Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Dong ◽  
Jiani Fu ◽  
Yong Guan ◽  
Haisong Liu ◽  
Qing Wang ◽  
...  
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Jiaozhou Bay ◽  
Buffer Zone ◽  
Water Area ◽  
Landsat Images ◽  
Protection Measures ◽  
Water Index ◽  
Contour Model ◽  
Landsat Satellite

The coastline is located at the junction of the sea and the land, and it is essential for ecological environment. However, most existing methods can extract the coastline with obvious boundaries and cannot obtain the general coastline, including an intertidal zone and salt field. Accordingly, a new general coastline extraction method is proposed on the basis of an improved active contour model to extract the general coastline from remote sensing images. An improved active contour model was proposed to extract the water area by introducing aiming energy of water from the Modified Normalized Difference Water Index information. Then, mathematical morphology was applied to obtain the seawater area based on the extracted water area. Finally, the coastline was refined and generated by the improved active contour model in a buffer zone of the seawater boundary. Landsat images over Jiaozhou Bay in Shandong Province, China, from 1990 to 2018 were used to extract the general coastline. Results demonstrate that the proposed method can effectively extract the general coastline, which is close to the reference coastline. The length of the coastline decreased from 234.64 km in 1990 to 221.21 km in 2000. This value significantly increased to 255.05 km from 2000 to 2010. The main reason is that Hongdao Island merged with the mainland due to reclamation. The length of the coastline slightly decreased by approximately 12 km from 2010 to 2018 due to environmental protection measures and the reclamation prohibition.

scholarly journals Active Contour Model with Adaptive weighted function for Robust Image Segmentation under Biased Conditions

2021 ◽  
pp. 114811
Author(s):  
Aditi Joshi ◽  
Mohammed Saquib Khan ◽  
Asim Niaz ◽  
Farhan Akram ◽  
Hyun Chul Song ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Weighted Function ◽  
Contour Model ◽  
Robust Image

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 Using Fast Fourier Transformation for Salient Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 192
Author(s):  
Umer Sadiq Khan ◽  
Xingjun Zhang ◽  
Yuanqi Su
Keyword(s):  
Object Detection ◽  
Fourier Transformation ◽  
Active Contour ◽  
Active Contour Model ◽  
Salient Object Detection ◽  
Fast Fourier Transformation ◽  
Salient Object ◽  
Active Contour Models ◽  
Contour Model ◽  
Contour Models

The active contour model is a comprehensive research technique used for salient object detection. Most active contour models of saliency detection are developed in the context of natural scenes, and their role with synthetic and medical images is not well investigated. Existing active contour models perform efficiently in many complexities but facing challenges on synthetic and medical images due to the limited time like, precise automatic fitted contour and expensive initialization computational cost. Our intention is detecting automatic boundary of the object without re-initialization which further in evolution drive to extract salient object. For this, we propose a simple novel derivative of a numerical solution scheme, using fast Fourier transformation (FFT) in active contour (Snake) differential equations that has two major enhancements, namely it completely avoids the approximation of expansive spatial derivatives finite differences, and the regularization scheme can be generally extended more. Second, FFT is significantly faster compared to the traditional solution in spatial domain. Finally, this model practiced Fourier-force function to fit curves naturally and extract salient objects from the background. Compared with the state-of-the-art methods, the proposed method achieves at least a 3% increase of accuracy on three diverse set of images. Moreover, it runs very fast, and the average running time of the proposed methods is about one twelfth of the baseline.

Active contour model in cytological image recognition

1995 ◽  
Author(s):  
Vit. S. Medovy ◽  
A. V. Ivanov ◽  
Irina A. Ivanova ◽  
Vladimir S. Medovy ◽  
Natalya V. Verdenskaya
Keyword(s):  
Image Recognition ◽  
Active Contour ◽  
Active Contour Model ◽  
Contour Model
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