Bin Dong , Guirong Weng , Qianqian Bu , Zicong Zhu , Jingen Ni
{"title":"An active contour model based on shadow image and reflection edge for image segmentation","authors":"Bin Dong , Guirong Weng , Qianqian Bu , Zicong Zhu , Jingen Ni","doi":"10.1016/j.eswa.2023.122330","DOIUrl":null,"url":null,"abstract":"<div><p>Image segmentation is popular in many applications. Active contour models (ACMs) are very useful methods for image segmentation. However, many existing ACMs have drawbacks, e.g., obtaining poor performance for segmenting images with intensity inhomogeneity, or excessive convolution operations increasing calculation time. To solve these problems, a novel ACM based on shadow image and reflection edge (SIRE) is proposed, which represents the image by an additive model with the shadow image and the reflection edge. The shadow image is calculated with mean filtering, and the reflection edge is calculated by the optimal solution of the data driven term within the energy function. The image energy function is minimized by the level set method (LSM), by which the image segmentation is realized. The difference between the background and the target is adequately reflected by the reflection edge, which drives the evolution of the contour lines to find the target edge correctly. In the level set calculation, the optimized length term and the distance regularization term are used to improve the model robustness. Experimental results demonstrate that the proposed method can effectively segment inhomogeneous images, and that our model outperforms other three ACMs in terms of segmentation speed and accuracy.</p></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957417423028324","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Image segmentation is popular in many applications. Active contour models (ACMs) are very useful methods for image segmentation. However, many existing ACMs have drawbacks, e.g., obtaining poor performance for segmenting images with intensity inhomogeneity, or excessive convolution operations increasing calculation time. To solve these problems, a novel ACM based on shadow image and reflection edge (SIRE) is proposed, which represents the image by an additive model with the shadow image and the reflection edge. The shadow image is calculated with mean filtering, and the reflection edge is calculated by the optimal solution of the data driven term within the energy function. The image energy function is minimized by the level set method (LSM), by which the image segmentation is realized. The difference between the background and the target is adequately reflected by the reflection edge, which drives the evolution of the contour lines to find the target edge correctly. In the level set calculation, the optimized length term and the distance regularization term are used to improve the model robustness. Experimental results demonstrate that the proposed method can effectively segment inhomogeneous images, and that our model outperforms other three ACMs in terms of segmentation speed and accuracy.
期刊介绍:
Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.