利用空间关系分割肺区域的病灶检测

Donia Ben Hassen, H. Taleb
{"title":"利用空间关系分割肺区域的病灶检测","authors":"Donia Ben Hassen, H. Taleb","doi":"10.1109/ICITES.2012.6216669","DOIUrl":null,"url":null,"abstract":"In this paper, we have described a lesion detection approach from chest radiography. We have illustrated the importance of accurate segmentation as a preprocessing step in a CAD scheme. Then, a suitable combination among 118 features has been identified using the forward stepwise selection method. The main idea is to obtain a set of features that is enable a CAD not to discriminate between normal lesions and abnormal ones but to specify its nature if this lesion is an infection for example.","PeriodicalId":137864,"journal":{"name":"2012 International Conference on Information Technology and e-Services","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Lesion detection in lung regions that are segmented using spatial relations\",\"authors\":\"Donia Ben Hassen, H. Taleb\",\"doi\":\"10.1109/ICITES.2012.6216669\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we have described a lesion detection approach from chest radiography. We have illustrated the importance of accurate segmentation as a preprocessing step in a CAD scheme. Then, a suitable combination among 118 features has been identified using the forward stepwise selection method. The main idea is to obtain a set of features that is enable a CAD not to discriminate between normal lesions and abnormal ones but to specify its nature if this lesion is an infection for example.\",\"PeriodicalId\":137864,\"journal\":{\"name\":\"2012 International Conference on Information Technology and e-Services\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Information Technology and e-Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICITES.2012.6216669\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Information Technology and e-Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICITES.2012.6216669","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

在本文中,我们描述了一种从胸部x线摄影中检测病变的方法。我们已经说明了准确分割作为CAD方案预处理步骤的重要性。然后,利用正演逐步选择方法,在118个特征中找到合适的组合。主要思想是获得一组特征,使CAD不区分正常病变和异常病变,而是指定其性质,如果这种病变是感染。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lesion detection in lung regions that are segmented using spatial relations
In this paper, we have described a lesion detection approach from chest radiography. We have illustrated the importance of accurate segmentation as a preprocessing step in a CAD scheme. Then, a suitable combination among 118 features has been identified using the forward stepwise selection method. The main idea is to obtain a set of features that is enable a CAD not to discriminate between normal lesions and abnormal ones but to specify its nature if this lesion is an infection for example.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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