Automated chromosome classification using wavelet-based band pattern descriptors

Qiang Wu, K. Castleman
{"title":"Automated chromosome classification using wavelet-based band pattern descriptors","authors":"Qiang Wu, K. Castleman","doi":"10.1109/CBMS.2000.856898","DOIUrl":null,"url":null,"abstract":"Automated chromosome classification has been an important pattern recognition problem for decades. Numerous attempts were made in the past to characterize chromosome band patterns as part of the feature description vector. In this paper we describe a recent study to employ wavelet packets as basis function sets to compute chromosome band pattern features. A total of 28 wavelet packet basis function sets were evaluated in this study. The experimental results are presented and compared with those currently best-performing method on two benchmark chromosome datasets.","PeriodicalId":189930,"journal":{"name":"Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2000.856898","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 32

Abstract

Automated chromosome classification has been an important pattern recognition problem for decades. Numerous attempts were made in the past to characterize chromosome band patterns as part of the feature description vector. In this paper we describe a recent study to employ wavelet packets as basis function sets to compute chromosome band pattern features. A total of 28 wavelet packet basis function sets were evaluated in this study. The experimental results are presented and compared with those currently best-performing method on two benchmark chromosome datasets.
基于小波的条带模式描述子的染色体自动分类
几十年来,染色体自动分类一直是一个重要的模式识别问题。在过去,人们曾多次尝试将染色体带模式作为特征描述向量的一部分进行表征。本文介绍了利用小波包作为基函数集计算染色体带型特征的最新研究。本研究共评估了28个小波包基函数集。在两个基准染色体数据集上给出了实验结果,并与目前性能最好的方法进行了比较。
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:481959085
Book学术官方微信