{"title":"基于混合水平集的医学图像分割方法","authors":"Yan Zhang, B. Matuszewski, L. Shark, C. Moore","doi":"10.1109/MEDIVIS.2008.12","DOIUrl":null,"url":null,"abstract":"In this paper, a new hybrid medical image segmentation method in the level-set framework is proposed. The method uses both the objectpsilas boundary and region information to achieve robust and accurate segmentation results. The boundary information can help to detect the precise location of the target object and the region information can help to prevent the boundary leakage problem. Experimental results on a synthetic image as well as real 2D and 3D medical images are also shown in the paper with the emphasis on the comparisons between the new hybrid method and the Chan-Vese method.","PeriodicalId":51800,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","volume":"108 1","pages":"71-76"},"PeriodicalIF":1.3000,"publicationDate":"2008-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"224","resultStr":"{\"title\":\"Medical Image Segmentation Using New Hybrid Level-Set Method\",\"authors\":\"Yan Zhang, B. Matuszewski, L. Shark, C. Moore\",\"doi\":\"10.1109/MEDIVIS.2008.12\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a new hybrid medical image segmentation method in the level-set framework is proposed. The method uses both the objectpsilas boundary and region information to achieve robust and accurate segmentation results. The boundary information can help to detect the precise location of the target object and the region information can help to prevent the boundary leakage problem. Experimental results on a synthetic image as well as real 2D and 3D medical images are also shown in the paper with the emphasis on the comparisons between the new hybrid method and the Chan-Vese method.\",\"PeriodicalId\":51800,\"journal\":{\"name\":\"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization\",\"volume\":\"108 1\",\"pages\":\"71-76\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2008-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"224\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MEDIVIS.2008.12\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MEDIVIS.2008.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Medical Image Segmentation Using New Hybrid Level-Set Method
In this paper, a new hybrid medical image segmentation method in the level-set framework is proposed. The method uses both the objectpsilas boundary and region information to achieve robust and accurate segmentation results. The boundary information can help to detect the precise location of the target object and the region information can help to prevent the boundary leakage problem. Experimental results on a synthetic image as well as real 2D and 3D medical images are also shown in the paper with the emphasis on the comparisons between the new hybrid method and the Chan-Vese method.
期刊介绍:
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization is an international journal whose main goals are to promote solutions of excellence for both imaging and visualization of biomedical data, and establish links among researchers, clinicians, the medical technology sector and end-users. The journal provides a comprehensive forum for discussion of the current state-of-the-art in the scientific fields related to imaging and visualization, including, but not limited to: Applications of Imaging and Visualization Computational Bio- imaging and Visualization Computer Aided Diagnosis, Surgery, Therapy and Treatment Data Processing and Analysis Devices for Imaging and Visualization Grid and High Performance Computing for Imaging and Visualization Human Perception in Imaging and Visualization Image Processing and Analysis Image-based Geometric Modelling Imaging and Visualization in Biomechanics Imaging and Visualization in Biomedical Engineering Medical Clinics Medical Imaging and Visualization Multi-modal Imaging and Visualization Multiscale Imaging and Visualization Scientific Visualization Software Development for Imaging and Visualization Telemedicine Systems and Applications Virtual Reality Visual Data Mining and Knowledge Discovery.