颜色对比度增强预选宫颈细胞ThinPrep®图像

N. Mustafa, N. Isa, M. Y. Mashor, N. Othman
{"title":"颜色对比度增强预选宫颈细胞ThinPrep®图像","authors":"N. Mustafa, N. Isa, M. Y. Mashor, N. Othman","doi":"10.1109/IIH-MSP.2007.363","DOIUrl":null,"url":null,"abstract":"ThinPrepreg monolayer cytology was introduced to overcome the limitations of conventional Pap smear test for screening of cervical cancer. The cytological features in ThinPrepreg images could be improved if unwanted background information and poor contrast could be eliminated. This study proposes a contrast enhancement technique, which is only applied on the cervical cell of interest. The proposed technique is divided into two stages. Firstly, the cervical cells of interest will be selected using the modified seed based region growing algorithm. Then, the contrast of cervical cell of interest in the ThinPrepreg will be enhanced by using three contrast enhancement algorithms. The cervical cell of interest will be applied with linear contrast algorithm and the proposed nonlinear algorithms namely non-linear bright and nonlinear dark contrast to enhance the contrast of the ThinPrepreg images. The results show that the proposed technique improves the image quality. Hence, the resultant image would be rendered more useful for further analysis by pathologists.","PeriodicalId":385132,"journal":{"name":"Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Colour Contrast Enhancement on Preselected Cervical Cell for ThinPrep® Images\",\"authors\":\"N. Mustafa, N. Isa, M. Y. Mashor, N. Othman\",\"doi\":\"10.1109/IIH-MSP.2007.363\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ThinPrepreg monolayer cytology was introduced to overcome the limitations of conventional Pap smear test for screening of cervical cancer. The cytological features in ThinPrepreg images could be improved if unwanted background information and poor contrast could be eliminated. This study proposes a contrast enhancement technique, which is only applied on the cervical cell of interest. The proposed technique is divided into two stages. Firstly, the cervical cells of interest will be selected using the modified seed based region growing algorithm. Then, the contrast of cervical cell of interest in the ThinPrepreg will be enhanced by using three contrast enhancement algorithms. The cervical cell of interest will be applied with linear contrast algorithm and the proposed nonlinear algorithms namely non-linear bright and nonlinear dark contrast to enhance the contrast of the ThinPrepreg images. The results show that the proposed technique improves the image quality. Hence, the resultant image would be rendered more useful for further analysis by pathologists.\",\"PeriodicalId\":385132,\"journal\":{\"name\":\"Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007)\",\"volume\":\"134 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IIH-MSP.2007.363\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIH-MSP.2007.363","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

摘要

为了克服传统巴氏涂片检查筛查子宫颈癌的局限性,引入了薄胎膜单层细胞学。如果消除不必要的背景信息和对比度差,可以改善ThinPrepreg图像的细胞学特征。本研究提出了一种对比度增强技术,仅适用于感兴趣的宫颈细胞。所提出的技术分为两个阶段。首先,使用改进的基于种子的区域生长算法选择感兴趣的子宫颈细胞;然后,使用三种对比度增强算法增强ThinPrepreg中感兴趣的宫颈细胞的对比度。将感兴趣的宫颈细胞应用线性对比度算法和所提出的非线性算法,即非线性亮对比度和非线性暗对比度来增强ThinPrepreg图像的对比度。结果表明,该方法提高了图像质量。因此,结果图像将呈现更有用的进一步分析病理学家。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Colour Contrast Enhancement on Preselected Cervical Cell for ThinPrep® Images
ThinPrepreg monolayer cytology was introduced to overcome the limitations of conventional Pap smear test for screening of cervical cancer. The cytological features in ThinPrepreg images could be improved if unwanted background information and poor contrast could be eliminated. This study proposes a contrast enhancement technique, which is only applied on the cervical cell of interest. The proposed technique is divided into two stages. Firstly, the cervical cells of interest will be selected using the modified seed based region growing algorithm. Then, the contrast of cervical cell of interest in the ThinPrepreg will be enhanced by using three contrast enhancement algorithms. The cervical cell of interest will be applied with linear contrast algorithm and the proposed nonlinear algorithms namely non-linear bright and nonlinear dark contrast to enhance the contrast of the ThinPrepreg images. The results show that the proposed technique improves the image quality. Hence, the resultant image would be rendered more useful for further analysis by pathologists.
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