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3D digital image processing for biofilm quantification from confocal laser scanning microscopy: Multidimensional statistical analysis of biofilm modeling.

机译：共聚焦激光扫描显微镜用于生物膜定量的3D数字图像处理：生物膜建模的多维统计分析。

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摘要

The dramatic increase in number and volume of digital images produced in medical diagnostics, and the escalating demand for rapid access to these relevant medical data, along with the need for interpretation and retrieval has become of paramount importance to a modern healthcare system. Therefore, there is an ever growing need for processed, interpreted and saved images of various types. Due to the high cost and unreliability of human-dependent image analysis, it is necessary to develop an automated method for feature extraction, using sophisticated mathematical algorithms and reasoning.;This work is focused on digital image signal processing of biological and biomedical data in one- two- and three-dimensional space. Methods and algorithms presented in this work were used to acquire data from genomic sequences, breast cancer, and biofilm images. One-dimensional analysis was applied to DNA sequences which were presented as a non-stationary sequence and modeled by a time-dependent autoregressive moving average (TD-ARMA) model. Two-dimensional analyses used 2D-ARMA model and applied it to detect breast cancer from x-ray mammograms or ultrasound images. Three-dimensional detection and classification techniques were applied to biofilm images acquired using confocal laser scanning microscopy.;Modern medical images are geometrically arranged arrays of data. The broadening scope of imaging as a way to organize our observations of the biophysical world has led to a dramatic increase in our ability to apply new processing techniques and to combine multiple channels of data into sophisticated and complex mathematical models of physiological function and dysfunction. With explosion of the amount of data produced in a field of biomedicine, it is crucial to be able to construct accurate mathematical models of the data at hand. Two main purposes of signal modeling are: data size conservation and parameter extraction. Specifically, in biomedical imaging we have four key problems that were addressed in this work: (i) registration, i.e. automated methods of data acquisition and the ability to align multiple data sets with each other; (ii) visualization and reconstruction, i.e. the environment in which registered data sets can be displayed on a plane or in multidimensional space; (iii) segmentation, i.e. automated and semi-automated methods to create models of relevant anatomy from images; (iv) simulation and prediction, i.e. techniques that can be used to simulate growth end evolution of researched phenomenon. Mathematical models can not only be used to verify experimental findings, but also to make qualitative and quantitative predictions, that might serve as guidelines for the future development of technology and/or treatment.
机译：医疗诊断中产生的数字图像的数量和数量的急剧增加，以及对快速访问这些相关医学数据的需求不断增长，以及对解释和检索的需求，已成为现代医疗保健系统的重中之重。因此，对处理，解释和保存的各种类型的图像的需求不断增长。由于依赖人的图像分析的成本高且不可靠，因此有必要使用复杂的数学算法和推理方法来开发一种自动的特征提取方法。该工作集中于一个生物和生物医学数据的数字图像信号处理-二维和三维空间。这项工作中介绍的方法和算法用于从基因组序列，乳腺癌和生物膜图像中获取数据。一维分析应用于DNA序列，该序列以非平稳序列的形式呈现，并通过时间相关的自回归移动平均值（TD-ARMA）模型进行建模。二维分析使用2D-ARMA模型，并将其应用于从X射线乳房X线照片或超声图像中检测乳腺癌。三维检测和分类技术应用于通过共聚焦激光扫描显微镜获得的生物膜图像。现代医学图像是几何排列的数据阵列。作为组织我们对生物物理世界的观察的一种方式，影像学的范围不断扩大，导致我们应用新处理技术并将多种数据通道组合成生理功能和功能障碍的复杂数学模型的能力得到了显着提高。随着在生物医学领域中产生的数据量的爆炸式增长，能够构建手头数据的精确数学模型至关重要。信号建模的两个主要目的是：数据大小保留和参数提取。具体而言，在生物医学成像中，我们在这项工作中解决了四个关键问题：（i）注册，即数据采集的自动方法以及使多个数据集相互对齐的能力； （ii）可视化和重建，即可以在平面上或多维空间中显示已注册数据集的环境； （iii）分割，即从图像创建相关解剖模型的自动和半自动方法； （iv）模拟和预测，即可以用来模拟所研究现象的生长终点演变的技术。数学模型不仅可以用于验证实验结果，还可以进行定性和定量的预测，这可以作为未来技术和/或治疗发展的指南。

著录项

  • 作者

    Zielinski, Jerzy S.;

  • 作者单位

    University of Arkansas at Little Rock.;

  • 授予单位 University of Arkansas at Little Rock.;
  • 学科 Engineering Biomedical.;Engineering Materials Science.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 119 p.
  • 总页数 119
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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