Image Segmentation Using Active Contours with Hessian-Based Gradient Vector Flow External Force

The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-06, Vol.22 (13), p.4956
Main Authors: Qian, Qianqian, Cheng, Ke, Qian, Wei, Deng, Qingchang, Wang, Yuanquan
Format: Article
Language:English
Subjects:
Anisotropy
Constraint modelling
Electric fields
Energy
gradient vector flow
Hessian matrices
Hessian matrix
Image processing
image structure
Methods
Partial differential equations
Scientific papers
Smoothness
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Summary:The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint formula of the GVF model is re-expressed in matrix form, and the image knot represented by the Hessian matrix is included in the GVF model. Through the processing of this process, the relevant diffusion partial differential equation has anisotropy. The GVF model based on the Hessian matrix (HBGVF) has many advantages over other relevant GVF methods, such as accurate convergence to various concave surfaces, excellent weak edge retention ability, and so on. The following will prove the advantages of our proposed model through theoretical analysis and various comparative experiments.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22134956