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Object- and spatial-level quantitative analysis of multispectral histopathology images for detection and characterization of cancer.

机译：对多光谱组织病理学图像进行对象和空间水平的定量分析，以检测和表征癌症。

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摘要

The main goal of this dissertation is the development and discussion of techniques for higher-level image analysis, i.e., object-level analysis, of breast cancer imagery. Established cytologic (cell) criteria can be contradictory, and even histologic (tissue) criteria (considered the gold standard for diagnosis) are subject to varied interpretation. There is thus a need to quantitatively define characteristics of breast cancer to bettor coordinate clinical care of women presenting breast masses. We propose here an approach for such quantitative analysis, Quantitative Object- and spatial Arrangement-Level Analysis (QOALA), using expert (pathologist) input to guide the classification process.;The main contributions in this work are four-fold. First, quantitatively analyze the utility of multispectral imagery for classification and segmentation tasks in histopathology imagery. Second, we develop object-level segmentations for several histologic classes, as a quantitative object-level segmentation metric. Third, we extract a comprehensive set of both object- and spatial-level features which are used in a feature selection framework for classification of objects and imagery. Fourth, we extend the concepts of object-level features to higher-level image objects, analyze the utility of these high-level objects for image classification, and introduce the concept of a probabilistic graph-based model of imagery.;Overall, QOALA yields very good object- and image-level classification performances. More specifically, the object-level features as implemented in QOALA are versatile and general enough to elicit important information from even imperfectly segmented objects. Additionally, the use of non-nuclear features, namely features of cytoplasm and stoma have good classification performance, often exceeding that of nuclei. Higher-level features display a potential to increase both object- and image-level classification performance.
机译：本文的主要目的是开发和讨论乳腺癌图像的高级图像分析技术，即对象级分析。既定的细胞学（细胞）标准可能是矛盾的，甚至组织学（组织）标准（被认为是诊断的金标准）也有不同的解释。因此，需要定量地定义乳腺癌的特征，以更好地协调出现乳腺肿块的妇女的临床护理。我们在这里提出一种用于这种定量分析的方法，即使用专家（病理学家）的输入来指导分类过程的定量对象和空间排列水平分析（QOALA）。这项工作的主要贡献有四个方面。首先，定量分析多光谱图像在组织病理学图像中用于分类和分割任务的效用。其次，我们为几种组织学类别开发了对象级细分，以作为定量的对象级细分指标。第三，我们提取了对象和空间级别特征的综合集合，这些特征集在特征选择框架中用于对对象和图像进行分类。第四，我们将对象级特征的概念扩展到更高级别的图像对象，分析这些高级对象在图像分类中的效用，并介绍基于概率图的图像模型的概念。非常好的对象和图像级别的分类性能。更具体地说，在QOALA中实现的对象级功能是通用且通用的，足以从不完美分割的对象中获取重要信息。另外，使用非核特征，即细胞质和造口的特征具有良好的分类性能，通常超过核的分类性能。更高级别的功能具有提高对象和图像级别分类性能的潜力。

著录项

  • 作者

    Boucheron, Laura E.;

  • 作者单位

    University of California, Santa Barbara.;

  • 授予单位 University of California, Santa Barbara.;
  • 学科 Engineering Electronics and Electrical.;Biology Bioinformatics.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 433 p.
  • 总页数 433
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:38:40

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