B. Nakhjavanlo, M. E. Gharehveran, Maryam Hajiesmaeili, T. Ellis, J. Dehmeshki
{"title":"A deformable model based on level sets for image segmentation","authors":"B. Nakhjavanlo, M. E. Gharehveran, Maryam Hajiesmaeili, T. Ellis, J. Dehmeshki","doi":"10.1109/ICCP.2012.6356184","DOIUrl":null,"url":null,"abstract":"This paper presents a new level set-based image segmentation method. First, a Gabor filter is used to suppress of noise in the extracted regions of interest and guide the motion of the evolving contour detection. Second, Green's theorem is used to develop a region-based energy function, combined with diffusion-based smoothing, to separate low contrast regions. Results are presented for it's application to a variety of real and synthetic images, particularly those exhibiting texture properties. The results indicate the method is more effective than traditional region-based level set methods in coping with intensity inhomogeneities, noisy and textured images.","PeriodicalId":406461,"journal":{"name":"2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCP.2012.6356184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents a new level set-based image segmentation method. First, a Gabor filter is used to suppress of noise in the extracted regions of interest and guide the motion of the evolving contour detection. Second, Green's theorem is used to develop a region-based energy function, combined with diffusion-based smoothing, to separate low contrast regions. Results are presented for it's application to a variety of real and synthetic images, particularly those exhibiting texture properties. The results indicate the method is more effective than traditional region-based level set methods in coping with intensity inhomogeneities, noisy and textured images.