Algorithms of Experimental Medical Data Analysis

Y. Hamad, Mohammed N. Qasim, Ayvar A. Rashid, Mohammed E. Seno
{"title":"Algorithms of Experimental Medical Data Analysis","authors":"Y. Hamad, Mohammed N. Qasim, Ayvar A. Rashid, Mohammed E. Seno","doi":"10.1109/CSASE48920.2020.9142094","DOIUrl":null,"url":null,"abstract":"The paper is devoted to the development of a computational technique for assessing the performance of tissue regeneration in an experiment using mesh nickel-titanium implants with shape memory. Observational data obtained from electron microscopy and classical histological examination are processed and analyzed by the use of proprietary algorithms and their modifications. This can significantly facilitate the procedure of data analysis and increase the accuracy of the estimates by 5–10%. As a computational method for examining the dynamics of the studied process and determining the internal geometrical characteristics of empirical images of objects concerned, the suggested technique includes algorithms of shearlet transform, wavelet transform and construction of elastic maps for efficient visualization of spatial data. The significant part of the recommended method is the computing means of visual data preprocessing for increasing the brightness and contrast of the examined images based on the Retinex technology. This part has a significant impact on the quality of applying the tools of the computer-based evaluation presented in this work.","PeriodicalId":254581,"journal":{"name":"2020 International Conference on Computer Science and Software Engineering (CSASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Computer Science and Software Engineering (CSASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSASE48920.2020.9142094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The paper is devoted to the development of a computational technique for assessing the performance of tissue regeneration in an experiment using mesh nickel-titanium implants with shape memory. Observational data obtained from electron microscopy and classical histological examination are processed and analyzed by the use of proprietary algorithms and their modifications. This can significantly facilitate the procedure of data analysis and increase the accuracy of the estimates by 5–10%. As a computational method for examining the dynamics of the studied process and determining the internal geometrical characteristics of empirical images of objects concerned, the suggested technique includes algorithms of shearlet transform, wavelet transform and construction of elastic maps for efficient visualization of spatial data. The significant part of the recommended method is the computing means of visual data preprocessing for increasing the brightness and contrast of the examined images based on the Retinex technology. This part has a significant impact on the quality of applying the tools of the computer-based evaluation presented in this work.
实验医学数据分析算法
本文致力于开发一种计算技术,用于评估具有形状记忆的网状镍钛植入物在实验中的组织再生性能。从电子显微镜和经典组织学检查中获得的观察数据通过使用专有算法及其修改进行处理和分析。这可以大大简化数据分析过程,并将估计的准确性提高5-10%。作为一种检测研究过程的动力学和确定有关对象经验图像内部几何特征的计算方法,建议的技术包括shearlet变换、小波变换和构造弹性图算法,以实现空间数据的高效可视化。推荐方法的重要部分是基于Retinex技术的视觉数据预处理的计算手段,以提高被检查图像的亮度和对比度。这部分对本工作中提出的基于计算机的评估工具的应用质量有重大影响。
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