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A hierarchical image processing approach for diagnostic analysis of microcirculation videos.

机译:用于微循环视频诊断分析的分层图像处理方法。

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摘要

Knowledge of the microcirculatory system has added significant value to the analysis of tissue oxygenation and perfusion. While developments in videomicroscopy technology have enabled medical researchers and physicians to observe the microvascular system, the available software tools are limited in their capabilities to determine quantitative features of microcirculation, either automatically or accurately. In particular, microvessel density has been a critical diagnostic measure in evaluating disease progression and a prognostic indicator in various clinical conditions. As a result, automated analysis of the microcirculatory system can be substantially beneficial in various real-time and off-line therapeutic medical applications, such as optimization of resuscitation.;This study focuses on the development of an algorithm to automatically segment microvessels, calculate the density of capillaries in microcirculatory videos, and determine the distribution of blood circulation. The proposed technique is divided into four major steps: video stabilization, video enhancement, segmentation and post-processing. The stabilization step estimates motion and corrects for the motion artifacts using an appropriate motion model. Video enhancement improves the visual quality of video frames through preprocessing, vessel enhancement and edge enhancement. The resulting frames are combined through an adjusted weighted median filter and the resulting frame is then thresholded using an entropic thresholding technique. Finally, a region growing technique is utilized to correct for the discontinuity of blood vessels. Using the final binary results, the most commonly used measure for the assessment of microcirculation, i.e. Functional Capillary Density (FCD), is calculated.;The designed technique is applied to video recordings of healthy and diseased human and animal samples obtained by MicroScan device based on Sidestream Dark Field (SDF) imaging modality. To validate the final results, the calculated FCD results are compared with the results obtained by blind detailed inspection of three medical experts, who have used AVA (Automated Vascular Analysis) semi-automated microcirculation analysis software. Since there is neither a fully automated accurate microcirculation analysis program, nor a publicly available annotated database of microcirculation videos, the results acquired by the experts are considered the gold standard. Bland-Altman plots show that there is "Good Agreement" between the results of the algorithm and that of gold standard.;In summary, the main objective of this study is to eliminate the need for human interaction to edit/ correct results, to improve the accuracy of stabilization and segmentation, and to reduce the overall computation time. The proposed methodology impacts the field of computer science through development of image processing techniques to discover the knowledge in grayscale video frames. The broad impact of this work is to assist physicians, medical researchers and caregivers in making diagnostic and therapeutic decisions for microcirculatory abnormalities and in studying of the human microcirculation.
机译:微循环系统的知识为组织充氧和灌注分析增加了重要价值。虽然视频显微镜技术的发展使医学研究人员和医师能够观察微血管系统,但是可用的软件工具在自动或准确地确定微循环定量特征方面的能力有限。尤其是,微血管密度已成为评估疾病进展的关键诊断指标和各种临床条件下的预后指标。因此,对微循环系统的自动分析在各种实时和离线治疗医学应用中(例如复苏的优化)都将大有裨益;该研究的重点是开发一种自动分割微血管,计算血管舒张率的算法。微循环视频中毛细血管的密度,并确定血液循环的分布。所提出的技术分为四个主要步骤:视频稳定,视频增强,分段和后处理。稳定步骤估计运动并使用适当的运动模型校正运动伪影。视频增强通过预处理,血管增强和边缘增强来改善视频帧的视觉质量。通过调整后的加权中值滤波器对结果帧进行组合,然后使用熵阈值技术对结果帧进行阈值化。最后,利用区域生长技术来纠正血管的不连续性。使用最终的二进制结果,计算出评估微循环最常用的量度,即功能性毛细血管密度(FCD)。设计的技术应用于通过MicroScan设备获得的健康和患病人类和动物样本的视频记录在侧流暗场(SDF)成像方式上。为了验证最终结果,将计算出的FCD结果与通过使用AVA(自动血管分析)半自动微循环分析软件的三位医学专家的盲目详细检查获得的结果进行比较。由于既没有全自动准确的微循环分析程序,也没有公开的微循环视频注释数据库,因此专家获得的结果被视为黄金标准。 Bland-Altman图显示算法的结果与黄金标准的结果之间存在“良好的一致性”。总之,本研究的主要目标是消除需要人工干预来编辑/校正结果,以改进稳定和分割的准确性,并减少了整体计算时间。所提出的方法通过开发图像处理技术来发现灰度视频帧中的知识,从而影响了计算机科学领域。这项工作的广泛影响是协助医师,医学研究人员和护理人员就微循环异常做出诊断和治疗决策以及研究人类微循环。

著录项

  • 作者

    Mirshahi, Nazanin.;

  • 作者单位

    Virginia Commonwealth University.;

  • 授予单位 Virginia Commonwealth University.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 103 p.
  • 总页数 103
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:44:09

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