基于多光谱纹理特征的二次分类器在前列腺癌诊断中的应用

M. A. Roula, A. Bouridane, F. Kurugollu, A. Amira
{"title":"基于多光谱纹理特征的二次分类器在前列腺癌诊断中的应用","authors":"M. A. Roula, A. Bouridane, F. Kurugollu, A. Amira","doi":"10.1109/ISSPA.2003.1224809","DOIUrl":null,"url":null,"abstract":"This paper is concerned with the development of an automatic classification system for use in prostate cancer diagnosis. The system aims to detect and classify nuclei textures captured from microscopic samples taken from needle biopsies. The main contribution here is that the analysis is carried out over thirty-three spectral bands instead of using the conventional grey scale or RGB colour spaces. A set of texture and morphological features has been computed for all these spectral bands for use in the discrimination phase. The large vector size has then been reduced to a manageable size by using a principal component analysis. Classification tests have been carried out using quadratic discriminant analysis and have shown that multispectral analysis significantly improves the overall classification performances when compared with the case where multispectral features are not considered.","PeriodicalId":264814,"journal":{"name":"Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":"{\"title\":\"A quadratic classifier based on multispectral texture features for prostate cancer diagnosis\",\"authors\":\"M. A. Roula, A. Bouridane, F. Kurugollu, A. Amira\",\"doi\":\"10.1109/ISSPA.2003.1224809\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper is concerned with the development of an automatic classification system for use in prostate cancer diagnosis. The system aims to detect and classify nuclei textures captured from microscopic samples taken from needle biopsies. The main contribution here is that the analysis is carried out over thirty-three spectral bands instead of using the conventional grey scale or RGB colour spaces. A set of texture and morphological features has been computed for all these spectral bands for use in the discrimination phase. The large vector size has then been reduced to a manageable size by using a principal component analysis. Classification tests have been carried out using quadratic discriminant analysis and have shown that multispectral analysis significantly improves the overall classification performances when compared with the case where multispectral features are not considered.\",\"PeriodicalId\":264814,\"journal\":{\"name\":\"Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings.\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"32\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPA.2003.1224809\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPA.2003.1224809","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 32

摘要

本文讨论了一种用于前列腺癌诊断的自动分类系统的开发。该系统旨在检测和分类从针活检的显微样本中捕获的细胞核结构。这里的主要贡献是分析是在33个光谱带上进行的,而不是使用传统的灰度或RGB色彩空间。计算了所有光谱波段的一组纹理和形态特征,用于识别阶段。然后,通过使用主成分分析,将大向量大小减小到可管理的大小。使用二次判别分析进行了分类测试,结果表明,与不考虑多光谱特征的情况相比,多光谱分析显著提高了整体分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A quadratic classifier based on multispectral texture features for prostate cancer diagnosis
This paper is concerned with the development of an automatic classification system for use in prostate cancer diagnosis. The system aims to detect and classify nuclei textures captured from microscopic samples taken from needle biopsies. The main contribution here is that the analysis is carried out over thirty-three spectral bands instead of using the conventional grey scale or RGB colour spaces. A set of texture and morphological features has been computed for all these spectral bands for use in the discrimination phase. The large vector size has then been reduced to a manageable size by using a principal component analysis. Classification tests have been carried out using quadratic discriminant analysis and have shown that multispectral analysis significantly improves the overall classification performances when compared with the case where multispectral features are not considered.
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:481959085
Book学术官方微信