{"title":"一种改进的MRI图像序列心脏边界识别与跟踪方法","authors":"B. Chaudhuri, A. Bhattacharya, S. Mitra, S. Dutta","doi":"10.1109/TENCON.2008.4766462","DOIUrl":null,"url":null,"abstract":"In the present work, a robust algorithm for automatic identification and segmentation of heart portion from cardiac Magnetic Resonance video Image (MRI) is presented. At first, an outline has been generated to get the region of interest (ROI) by employing the moving object criterion, which eventually reduces the processing time significantly. In the next step, Expectation Maximization (EM) algorithm is used to segment the grey scale images into 5 distinct regions. This is done to make them more suitable for further processing and easy to use in the developed software. Finally Level set algorithm added with automatic contour generation module is used for tracking the exact heart boundary to segment it out from the rest of the image. This algorithm gives equally persistent result for both long axis and shot axis cardiac MRI data consisting of a movie (in AVI format) containing 32 separate frames of grayscale images.","PeriodicalId":22230,"journal":{"name":"TENCON 2008 - 2008 IEEE Region 10 Conference","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A modified approach of identification and tracking of heart boundary from MRI image sequences\",\"authors\":\"B. Chaudhuri, A. Bhattacharya, S. Mitra, S. Dutta\",\"doi\":\"10.1109/TENCON.2008.4766462\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the present work, a robust algorithm for automatic identification and segmentation of heart portion from cardiac Magnetic Resonance video Image (MRI) is presented. At first, an outline has been generated to get the region of interest (ROI) by employing the moving object criterion, which eventually reduces the processing time significantly. In the next step, Expectation Maximization (EM) algorithm is used to segment the grey scale images into 5 distinct regions. This is done to make them more suitable for further processing and easy to use in the developed software. Finally Level set algorithm added with automatic contour generation module is used for tracking the exact heart boundary to segment it out from the rest of the image. This algorithm gives equally persistent result for both long axis and shot axis cardiac MRI data consisting of a movie (in AVI format) containing 32 separate frames of grayscale images.\",\"PeriodicalId\":22230,\"journal\":{\"name\":\"TENCON 2008 - 2008 IEEE Region 10 Conference\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"TENCON 2008 - 2008 IEEE Region 10 Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TENCON.2008.4766462\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"TENCON 2008 - 2008 IEEE Region 10 Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TENCON.2008.4766462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A modified approach of identification and tracking of heart boundary from MRI image sequences
In the present work, a robust algorithm for automatic identification and segmentation of heart portion from cardiac Magnetic Resonance video Image (MRI) is presented. At first, an outline has been generated to get the region of interest (ROI) by employing the moving object criterion, which eventually reduces the processing time significantly. In the next step, Expectation Maximization (EM) algorithm is used to segment the grey scale images into 5 distinct regions. This is done to make them more suitable for further processing and easy to use in the developed software. Finally Level set algorithm added with automatic contour generation module is used for tracking the exact heart boundary to segment it out from the rest of the image. This algorithm gives equally persistent result for both long axis and shot axis cardiac MRI data consisting of a movie (in AVI format) containing 32 separate frames of grayscale images.