Adaptive active contour model driven by image data field for image segmentation with flexible initialization

2019 ◽  
Vol 78 (23) ◽  
pp. 33633-33658
Author(s):  
Yongfei Wu ◽  
Xilin Liu ◽  
Daoxiang Zhou ◽  
Yang Liu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Image Data ◽  
Model Driven ◽  
Contour Model ◽  
Data Field

An active contour model driven by anisotropic region fitting energy for image segmentation

2013 ◽  
Vol 23 (1) ◽  
pp. 238-243 ◽  
Author(s):  
Qi Ge ◽  
Liang Xiao ◽  
Hu Huang ◽  
Zhi Hui Wei
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Model Driven ◽  
Contour Model

Image segmentation via active contour model driven by interregion contrast

2016 ◽  
Vol 25 (5) ◽  
pp. 053020 ◽  
Author(s):  
Cong Yang ◽  
Weiguo Wu ◽  
Yuanqi Su ◽  
Yiwei Wu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Model Driven ◽  
Contour Model

scholarly journals Hybrid Methodology for Image Segmentation Based on Active Contour Module and Alpha-Shape Theory

2016 ◽  
Vol 10 (11) ◽  
pp. 30
Author(s):  
Mohammed Sabbih Hamoud Al-Tamimi
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Image Data ◽  
Magnetic Resonance Images ◽  
Computational Method ◽  
X Rays ◽  
Alpha Shape ◽  
Contour Model ◽  
Objective Contour

The concept of the active contour model has been extensively utilized in the segmentation and analysis of images. This technology has been effectively employed in identifying the contours in object recognition, computer graphics and vision, biomedical processing of images that is normal images or medical images such as Magnetic Resonance Images (MRI), X-rays, plus Ultrasound imaging. Three colleagues, Kass, Witkin and Terzopoulos developed this energy, lessening “Active Contour Models” (equally identified as Snake) back in 1987. Being curved in nature, snakes are characterized in an image field and are capable of being set in motion by external and internal forces within image data and the curve itself in that order. The present study proposes the use of a hybrid image segmentation technique to acquire precise segmentation outcomes, while engaging “Alpha Shape (α-Shape)” in supposition to derive the original contour, followed by a refining process through engaging a conventional active contour model. Empirical results show high potential in the suggested computational method. Trials indicate that the primary contour is capable of being precisely set next to the objective contour and effectively have these objective contours extracted, devoid of any contour instigation. Some of the benefits associated with the novel hybrid contour include minimized cost of computation, enhanced anti-jamming capability, as well as enlarged utilization array of snake model.

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