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Image Segmentation Using Active Contours with Hessian-Based Gradient Vector Flow External Force
Image Segmentation Using Active Contours with Hessian-Based Gradient Vector Flow External Force
Qianqian Qian
(School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China)
Ke Cheng
(School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China)
Wei Qian
(School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China)
Qingchang Deng
(School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China)
Yuanquan Wang
(School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China)
Journal: Sensors
•
• MDPI AG
• ISSN: 1424-8220
Abstract
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.
Qianqian Qian, Ke Cheng, Wei Qian, et al. (2022). "Image Segmentation Using Active Contours with Hessian-Based Gradient Vector Flow External Force". Sensors. https://doi.org/10.3390/s22134956