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Efficient Processing of Corneal Confocal Microscopy Images. Development of a computer system for the pre-processing, feature extraction, classification, enhancement and registration of a sequence of corneal images.

机译:角膜共聚焦显微镜图像的高效处理。开发用于对一系列角膜图像进行预处理,特征提取,分类,增强和配准的计算机系统。

摘要

Corneal diseases are one of the major causes of visual impairment and blindness worldwide. Used for diagnoses, a laser confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, ophthalmologists can extract clinical information on the state of health of a patient’s cornea. However, many factors impede ophthalmologists in forming diagnoses starting with the large number and variable quality of the individual images (blurring, non-uniform illumination within images, variable illumination between images and noise), and there are also difficulties posed for automatic processing caused by eye movements in both lateral and axial directions during the scanning process. udAiding ophthalmologists working with long sequences of corneal image requires the development of new algorithms which enhance, correctly order and register the corneal images within a sequence. The novel algorithms devised for this purpose and presented in this thesis are divided into four main categories. The first is enhancement to reduce the problems within individual images. The second is automatic image classification to identify which part of the cornea each image belongs to, when they may not be in the correct sequence. The third is automatic reordering of the images to place the images in the right sequence. The fourth is automatic registration of the images with each other. A flexible application called CORNEASYS has been developed and implemented using MATLAB and the C language to provide and run all the algorithms and methods presented in this thesis. CORNEASYS offers users a collection of all the proposed approaches and algorithms in this thesis in one platform package. CORNEASYS also provides a facility to help the research team and Ophthalmologists, who are in discussions to determine future system requirements which meet clinicians’ needs.
机译:角膜疾病是全世界视力障碍和失明的主要原因之一。用于诊断的激光共聚焦显微镜以递增的深度提供各种角膜层和结构的图像序列。从中,眼科医生可以提取有关患者角膜健康状况的临床信息。然而,许多因素阻碍了眼科医生进行诊断,这些诊断从单个图像的数量大且质量可变(模糊,图像内照明不均匀,图像之间的照明可变以及噪声)开始,并且还存在由自动图像处理引起的困难。扫描过程中眼睛在横向和轴向上的运动。 ud协助眼科医生处理长序列的角膜图像需要开发新的算法,以增强,正确排序和配准序列中的角膜图像。为此目的而设计并在本文中提出的新算法分为四个主要类别。首先是增强功能以​​减少单个图像中的问题。第二种是自动图像分类,以识别每个图像可能不正确的顺序,从而识别每个图像属于角膜的哪一部分。第三是图像的自动重新排序,以按正确的顺序放置图像。第四是图像彼此自动配准。使用MATLAB和C语言开发并实现了一个名为CORNEASYS的灵活应用程序,以提供并运行本文提出的所有算法和方法。 CORNEASYS在一个平台程序包中为用户提供了本文中所有拟议的方法和算法的集合。 CORNEASYS还提供了一种工具来帮助正在讨论中的研究团队和眼科医生确定满足临床医生需求的未来系统要求。

著录项

  • 作者

    Elbita Abdulhakim Mehemed;

  • 作者单位
  • 年度 2013
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
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