{"title":"利用活动轮廓模型和偏微分方程进行图像分割","authors":"Č. Livada, Hrvoje Leventić, I. Galić","doi":"10.1109/ELMAR.2014.6923348","DOIUrl":null,"url":null,"abstract":"This article displays benefits of image segmentation for purpose of 3D reconstruction. Medical images and in this case CT slices are being reconstructed in 3D space for better medical investigation. It is relatively hard to use raw images for 3D reconstruction, so certain areas are being singled out using active contour models. Partial Differential Equations (PDEs), as non-linear diffusion, are used for image denoising because CT images have large gradiental areas that need to be evened out.","PeriodicalId":424325,"journal":{"name":"Proceedings ELMAR-2014","volume":"122 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image segmentation using active contour models and partial differential equations\",\"authors\":\"Č. Livada, Hrvoje Leventić, I. Galić\",\"doi\":\"10.1109/ELMAR.2014.6923348\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article displays benefits of image segmentation for purpose of 3D reconstruction. Medical images and in this case CT slices are being reconstructed in 3D space for better medical investigation. It is relatively hard to use raw images for 3D reconstruction, so certain areas are being singled out using active contour models. Partial Differential Equations (PDEs), as non-linear diffusion, are used for image denoising because CT images have large gradiental areas that need to be evened out.\",\"PeriodicalId\":424325,\"journal\":{\"name\":\"Proceedings ELMAR-2014\",\"volume\":\"122 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings ELMAR-2014\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ELMAR.2014.6923348\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings ELMAR-2014","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ELMAR.2014.6923348","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image segmentation using active contour models and partial differential equations
This article displays benefits of image segmentation for purpose of 3D reconstruction. Medical images and in this case CT slices are being reconstructed in 3D space for better medical investigation. It is relatively hard to use raw images for 3D reconstruction, so certain areas are being singled out using active contour models. Partial Differential Equations (PDEs), as non-linear diffusion, are used for image denoising because CT images have large gradiental areas that need to be evened out.