Active contour model driven by Self Organizing Maps for image segmentation. (1st September 2021)

• Record Type: Journal Article
• Authors: Dong, Bin; Weng, Guirong; Jin, Ri
• Is Part Of: Expert systems with applications. Volume 177(2021)
• Publication Date: 2021-09-01
• Subjects: Active contour model -- Self Organizing Maps -- Intensity inhomogeneity -- Image segmentation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.114948
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A new function for computing local self-organizing clustering center with SOM clustering algorithm. An adaptive sign function is presented to replace the constant of the area term in the level set method that enables model to change motion direction of the level set. An improved double-well potential function is proposed to improve the stability of curve evolution. The proposed model shows better segmentation accuracy and robustness. Abstract: Supervised active contour models can use information extracted from supervised samples to guide contour evolution. However, their applicability is limited by the accuracy of the probabilistic models they use, especially when processing images with intensity inhomogeneity. In this paper, an unsupervised activity contour model with Self Organizing Maps (SOM) is proposed. The proposed model employs the self-organizing neural network to perform clustering calculation and the clustering center is called local self-organizing clustering center. An adaptive sign function is used to control the direction of curve evolution. To improve the stability of curve evolution, an improved double-well potential function is proposed. The experiment results show that our model can effectively segment images with intensity inhomogeneity.