A robust active contour model driven by pre-fitting bias correction and optimized fuzzy c-means algorithm for fast image segmentation

2019 ◽  
Vol 359 ◽  
pp. 408-419 ◽  
Author(s):  
Ri Jin ◽  
Guirong Weng
Keyword(s):  
Image Segmentation ◽  
Bias Correction ◽  
Active Contour ◽  
Active Contour Model ◽  
Fuzzy C Means ◽  
Model Driven ◽  
Contour Model ◽  
Fuzzy C Means Algorithm

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

A Novel Active Contour Model for Image Segmentation and Bias Correction Using Guided Image Filtering Regularization Term

2017 ◽  
Vol 31 (07) ◽  
pp. 1754013 ◽  
Author(s):  
Haijun Wang ◽  
Ming Liu
Keyword(s):  
Image Segmentation ◽  
Bias Correction ◽  
Active Contour ◽  
Spatial Information ◽  
Active Contour Model ◽  
Image Filtering ◽  
Distribution Fitting ◽  
Contour Model ◽  
Guided Image Filtering ◽  
Local Image

This paper presents a novel active contour model for image segmentation and bias correction in terms of robustness to initialization and intensity inhomogeneity. In our model, the local image intensities are described by Gaussian distributions with different means and variances. The local Gaussian distribution fitting energy with a new guided image filtering (GIF) regularization is proposed. The new guided image regularization not only considers the spatial information, but also utilizes the local image content. So compared with the traditional algorithms, the proposed model is less sensitive to initialization and converges faster. Comparative experiments show the advantage of the proposed method.

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