{"title":"Segmentation of MR osteosarcoma images","authors":"Jincheng Pan, Minglu Li","doi":"10.1109/ICCIMA.2003.1238155","DOIUrl":null,"url":null,"abstract":"There is a large body of literature about MR image segmentation methods. In this paper we briefly review these methods, particular emphasis is based on the relative merits of single image versus multispectral segmentation, and supervised versus unsupervised segmentation methods. Finally, we discuss that how to segment osteosarcoma into tumor tissue classes based on three different MR weighted image parameters (T1, PD, and T2) using unsupervised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIMA.2003.1238155","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

There is a large body of literature about MR image segmentation methods. In this paper we briefly review these methods, particular emphasis is based on the relative merits of single image versus multispectral segmentation, and supervised versus unsupervised segmentation methods. Finally, we discuss that how to segment osteosarcoma into tumor tissue classes based on three different MR weighted image parameters (T1, PD, and T2) using unsupervised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition.
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MR骨肉瘤图像的分割
关于MR图像分割方法有大量的文献。本文简要介绍了这些方法,重点介绍了单图像与多光谱分割、监督与无监督分割方法的相对优点。最后,我们讨论了如何基于三种不同的MR加权图像参数(T1, PD和T2),使用无监督模糊c均值(FCM)聚类算法进行模式识别,将骨肉瘤划分为肿瘤组织类别。
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