文献类型：期刊文献

英文题名：Global-to-local region-based indicator embedded in edge-based level set model for segmentation

作者：Zhou, Zhiheng[1,2];Dai, Ming[3];Guo, Yongfan[1];Li, Xiangwei[4]

机构：[1]South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Peoples R China;[2]South China Univ Technol, Minist Educ, Key Lab Big Data & Intelligent Robot, Guangzhou 510640, Peoples R China;[3]Guangdong Ocean Univ, Fac Math & Comp Sci, Zhanjiang 524088, Peoples R China;[4]Lanzhou Inst Technol, Sch Comp & Artificial Intelligence, Lanzhou 730050, Peoples R China

年份：2021

卷号：114

外文期刊名：DIGITAL SIGNAL PROCESSING

收录：SCI-EXPANDED(收录号：WOS:000663083100007)、、EI(收录号：20211710255606)、Scopus(收录号：2-s2.0-85104599366)、WOS

基金：The work is supported by National Key R&D Program of China (2018YFC0309400) , National Natural Science Foundation of China (61871188) , Guangzhou City Science and Technology Research Projects (201902020008) .

语种：英文

外文关键词：Image segmentation; Level set method; Edge information; Region information; Global-to-local

外文摘要：Image segmentation is an essential analysis tool in the field of computer vision, and the level set method has been widely used in image segmentation. Specifically, the edge-based level set models can reduce many undesired regions because they mainly rely on the edge information. However, the edge-based level set models are usually sensitive to the initial condition, which limits their application. To overcome this shortcoming, a global-to-local region-based indicator is designed in this paper, which is utilized to embed the region information into the edge-based models. Unlike the edge-based indicator frequently used in the edge-based models, the proposed region-based indicator can allow bidirectional motion of the active contour curve according to the region information. In general, the proposed region-based indicator can intrinsically incorporate the edge information and region information into one single energy function. Experimental results on synthetic images, natural images and medical images validate the effectiveness of the proposed method. Compared with some other level set models, the proposed method generally achieves better performance. (C) 2021 Elsevier Inc. All rights reserved.