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A study on diagnostic image analysis for the detection of precancerous lesions using multi-spectral digital images.

机译：利用多光谱数字图像对癌前病变进行诊断图像分析的研究。

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摘要

This dissertation explores a diagnostic image analysis framework using multispectral digital colposcopy for real-time in vivo detection of cervical cancer. In the first part of the dissertation, the clinical feasibility of a previously developed multispectral digital colposcope (MDC) is demonstrated using a hamster cheek pouch model of carcinogenesis. Various studies on MDC applications to cervical cancer detection in human subjects are then presented. First, an automated diagnostic image analysis algorithm for cervical cancer using white light reflectance images is presented. The algorithm can identify pre-neoplastic tissue areas from an entire cervix based on intensity changes feature in the reflectance images induced by acetic acid treatment. Then, the information about tissue type is incorporated into the diagnostic image analysis framework. For this purpose, a Markov Random Field (MRF) model is adopted and the results are discussed. One of the practical difficulties of utilizing a MRF model in unpolarized white light reflectance imaging is the specular reflection problem since the effect of specular reflection extends into surrounding tissue areas. Through the use of cross polarized imaging, the effects of specular reflection reduced and the ability to segment images based on tissue types is enhanced, leading to better diagnostic performance. The diagnostic performance of polarized imaging is compared to that of unpolarized imaging. In order to assess the performance of the proposed approach, a gold standard for the entire cervical image is constructed using histopathology results from a whole cervix specimen.; The results presented in this dissertation indicate that an automated diagnostic image analysis framework for early detection of cervical cancer has the potential to be clinically applied as a low cost alternative screening technique in developing countries. Advances in imaging technology as well as in image analysis algorithms will continue to reduce the cost of diagnostic imaging systems and improve the imaging and diagnostic capability, leading to an inexpensive, real-time, minimally-invasive alternative to conventional screening techniques for early detection of cervical cancer in developing countries.
机译：本文探索了一种利用多光谱数字阴道镜对宫颈癌进行实时体内检测的诊断图像分析框架。在论文的第一部分，使用致癌的仓鼠脸颊袋模型证明了先前开发的多光谱数字阴道镜（MDC）的临床可行性。然后介绍了有关MDC在人类受试者中检测宫颈癌的各种研究。首先，提出了一种使用白光反射率图像的宫颈癌自动诊断图像分析算法。该算法可以根据乙酸处理引起的反射率图像中的强度变化特征，从整个子宫颈识别出肿瘤前的组织区域。然后，有关组织类型的信息将合并到诊断图像分析框架中。为此，采用了马尔可夫随机场（MRF）模型并讨论了结果。在无偏振白光反射率成像中使用MRF模型的实际困难之一是镜面反射问题，因为镜面反射的影响扩展到周围的组织区域。通过使用交叉偏振成像，减少了镜面反射的影响，并增强了根据组织类型分割图像的能力，从而带来了更好的诊断性能。将偏振成像的诊断性能与非偏振成像的诊断性能进行比较。为了评估所提出方法的性能，使用整个子宫颈标本的组织病理学结果为整个子宫颈图像建立了黄金标准。本文提出的结果表明，一种用于宫颈癌早期检测的自动诊断图像分析框架具有在发展中国家作为低成本替代筛查技术进行临床应用的潜力。成像技术以及图像分析算法的进步将继续降低诊断成像系统的成本，并提高成像和诊断能力，从而为传统的筛查技术提供一种廉价，实时，微创的替代方案，以尽早发现癌症。发展中国家的子宫颈癌。

著录项

  • 作者

    Park, Sun Young.;

  • 作者单位

    The University of Texas at Austin.$bBiomedical Engineering Department.;

  • 授予单位 The University of Texas at Austin.$bBiomedical Engineering Department.;
  • 学科 Engineering Biomedical.; Computer Science.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 123 p.
  • 总页数 123
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物医学工程;自动化技术、计算机技术;
  • 关键词

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