Combinative multi-scale level set framework for echocardiographic image segmentation

2003 ◽  
Vol 7 (4) ◽  
pp. 529-537 ◽  
Author(s):  
Ning Lin ◽  
Weichuan Yu ◽  
James S. Duncan
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Scale Level ◽  
Multi Scale ◽  
Echocardiographic Image ◽  
Level Set Framework

A novel multi-scale level set method for SAR image segmentation based on a statistical model

2012 ◽  
Vol 33 (17) ◽  
pp. 5600-5614 ◽  
Author(s):  
Haigang Sui ◽  
Chuan Xu ◽  
Junyi Liu ◽  
Kaimin Sun ◽  
Chengfeng Wen
Keyword(s):  
Image Segmentation ◽  
Statistical Model ◽  
Level Set Method ◽  
Level Set ◽  
Sar Image ◽  
Scale Level ◽  
Multi Scale ◽  
Sar Image Segmentation

scholarly journals A weighted region-based level set method for image segmentation with intensity inhomogeneity

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255948
Author(s):  
Haiping Yu ◽  
Ping Sun ◽  
Fazhi He ◽  
Zhihua Hu
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
State Of The Art ◽  
Intensity Inhomogeneity ◽  
Curve Evolution ◽  
Pressure Force ◽  
The Stability ◽  
Level Set Framework ◽  
Robust To Noise

Image segmentation is a fundamental task in image processing and is still a challenging problem when processing images with high noise, low resolution and intensity inhomogeneity. In this paper, a weighted region-based level set method, which is based on the techniques of local statistical theory, level set theory and curve evolution, is proposed. Specifically, a new weighted pressure force function (WPF) is first presented to flexibly drive the closed contour to shrink or expand outside and inside of the object. Second, a faster and smoother regularization term is added to ensure the stability of the curve evolution and that there is no need for initialization in curve evolution. Third, the WPF is integrated into the region-based level set framework to accelerate the speed of the curve evolution and improve the accuracy of image segmentation. Experimental results on medical and natural images demonstrate that the proposed segmentation model is more efficient and robust to noise than other state-of-the-art models.

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