Fuzzy local Gaussian mixture model for brain MR image segmentation.

Zexuan Ji, Yong Xia, Quansen Sun, Qiang Chen, Deshen Xia, David Dagan Feng
{"title":"Fuzzy local Gaussian mixture model for brain MR image segmentation.","authors":"Zexuan Ji,&nbsp;Yong Xia,&nbsp;Quansen Sun,&nbsp;Qiang Chen,&nbsp;Deshen Xia,&nbsp;David Dagan Feng","doi":"10.1109/TITB.2012.2185852","DOIUrl":null,"url":null,"abstract":"<p><p>Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.</p>","PeriodicalId":55008,"journal":{"name":"IEEE Transactions on Information Technology in Biomedicine","volume":"16 3","pages":"339-47"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TITB.2012.2185852","citationCount":"120","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Information Technology in Biomedicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TITB.2012.2185852","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2012/1/24 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 120

Abstract

Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.

脑磁共振图像分割的模糊局部高斯混合模型。
从磁共振(MR)图像中准确分割脑组织是定量脑图像分析的重要步骤。然而,由于脑磁共振图像中存在噪声和强度不均匀性,许多分割算法的精度有限。本文假设每个体素邻域内的局部图像数据满足高斯混合模型(GMM),提出了用于脑磁共振图像自动分割的模糊局部GMM (FLGMM)算法。该算法通过最小化目标能量函数来估计最大后验概率的分割结果,其中利用截断高斯核函数施加空间约束,利用模糊隶属度平衡各GMM的贡献。我们将我们的算法与合成和临床数据中最先进的分割方法进行了比较。结果表明,该算法在很大程度上克服了噪声、低对比度和偏场带来的困难,大大提高了脑MR图像分割的精度。
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
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine 工程技术-计算机:跨学科应用
自引率
0.00%
发文量
1
审稿时长
4.8 months
×
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