Novel extraction and tumour detection method using histogram study and SVM classification

Sara Sandabad, A. Benba, Y. Tahri, A. Hammouch
{"title":"Novel extraction and tumour detection method using histogram study and SVM classification","authors":"Sara Sandabad, A. Benba, Y. Tahri, A. Hammouch","doi":"10.1504/IJSISE.2016.078262","DOIUrl":null,"url":null,"abstract":"In this article we present a new method for detecting and segmenting brain tumour regions weighted brain MRI in T1 (with contrast). This method consists of three main stages: (i) extracting the region of interest (brain) using our EMBE method; (ii) study and histogram analysis of the MRI image to create learning and initialise the classification algorithm will be applied later to retrieve and locate the tumour; (iii) tumour detection and classification using SVM into two classes: tumour class and no-tumour class. Our method will be completed by a characterisation of the tumour area by determining its geometric properties. This work will facilitate later the immense task radiologists to the significant number of MRI images have to deal with daily, and may also be a way for future researchers in order to develop other new methods and develop this research so interesting.","PeriodicalId":56359,"journal":{"name":"International Journal of Signal and Imaging Systems Engineering","volume":"9 1","pages":"202"},"PeriodicalIF":0.6000,"publicationDate":"2016-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJSISE.2016.078262","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Signal and Imaging Systems Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJSISE.2016.078262","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

Abstract

In this article we present a new method for detecting and segmenting brain tumour regions weighted brain MRI in T1 (with contrast). This method consists of three main stages: (i) extracting the region of interest (brain) using our EMBE method; (ii) study and histogram analysis of the MRI image to create learning and initialise the classification algorithm will be applied later to retrieve and locate the tumour; (iii) tumour detection and classification using SVM into two classes: tumour class and no-tumour class. Our method will be completed by a characterisation of the tumour area by determining its geometric properties. This work will facilitate later the immense task radiologists to the significant number of MRI images have to deal with daily, and may also be a way for future researchers in order to develop other new methods and develop this research so interesting.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于直方图研究和支持向量机分类的肿瘤提取与检测方法
在本文中,我们提出了一种新的方法来检测和分割脑肿瘤区域加权脑MRI在T1(与对比)。该方法包括三个主要阶段:(i)使用EMBE方法提取感兴趣的区域(大脑);(ii)对MRI图像进行研究和直方图分析,以创建学习和初始化分类算法,稍后将应用于检索和定位肿瘤;(iii)利用支持向量机对肿瘤进行检测和分类,分为肿瘤类和无肿瘤类两类。我们的方法将通过确定其几何特性来完成肿瘤区域的表征。这项工作将有助于以后放射科医生每天处理大量MRI图像的巨大任务,也可能是未来研究人员开发其他新方法的一种方式,并使这项研究变得如此有趣。
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 去求助
来源期刊
CiteScore
2.10
自引率
0.00%
发文量
0
期刊最新文献
Image correlation, non-uniformly sampled rotation displacement measurement estimation Computational simulation of human fovea Syntactic approach to reconstruct simple and complex medical images Computational simulation of human fovea Syntactic approach to reconstruct simple and complex medical images
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1