Automated Segmentation of the Corpus Callosum Midsagittal Surface Area

F. Seixas, A. S. Souza, A. A. S. D. Santos, D. Muchaluat-Saade
{"title":"Automated Segmentation of the Corpus Callosum Midsagittal Surface Area","authors":"F. Seixas, A. S. Souza, A. A. S. D. Santos, D. Muchaluat-Saade","doi":"10.1109/SIBGRAPI.2007.29","DOIUrl":null,"url":null,"abstract":"The non-invasive in vivo nature of magnetic resonance imaging (MRI) makes it the modality of choice of many neuroanatomical imaging studies. This paper discusses automatic brain structure segmentation based on previous knowledge on statistical models. The method is validated by an experiment involving magnetic resonance images acquired from 20 healthy adult individuals (10 men and 10 women). The results provide normative data of the midsagittal surface area of the corpus callosum from a 46-55 years old range group, splitting results by gender. Our results were also compared with data obtained from other authors, validating the correlation between brain volume and the area of this structure. The final goal of this work is computer-aided diagnosis for brain diseases.","PeriodicalId":434632,"journal":{"name":"XX Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2007)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"XX Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIBGRAPI.2007.29","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

The non-invasive in vivo nature of magnetic resonance imaging (MRI) makes it the modality of choice of many neuroanatomical imaging studies. This paper discusses automatic brain structure segmentation based on previous knowledge on statistical models. The method is validated by an experiment involving magnetic resonance images acquired from 20 healthy adult individuals (10 men and 10 women). The results provide normative data of the midsagittal surface area of the corpus callosum from a 46-55 years old range group, splitting results by gender. Our results were also compared with data obtained from other authors, validating the correlation between brain volume and the area of this structure. The final goal of this work is computer-aided diagnosis for brain diseases.
胼胝体正中矢状面区域的自动分割
磁共振成像(MRI)的非侵入性使其成为许多神经解剖成像研究的选择方式。本文讨论了基于统计模型知识的脑结构自动分割。该方法通过一项涉及20名健康成年人(10名男性和10名女性)的磁共振图像的实验得到验证。结果提供了46-55岁年龄组胼胝体正中矢状面面积的规范数据,并按性别划分结果。我们的结果也与其他作者获得的数据进行了比较,证实了脑容量与该结构面积之间的相关性。这项工作的最终目标是计算机辅助诊断脑部疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信