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      • College of Graduate and Postdoctoral Studies
      • Electronic Theses and Dissertations
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      • HARVEST
      • College of Graduate and Postdoctoral Studies
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      Retinal blood vessel segmentation: methods and implementations

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      JIANG-THESIS-2017.pdf (60.71Mb)
      Date
      2017-08-23
      Author
      Jiang, Zhexin 1992-
      ORCID
      0000-0002-7670-0826
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. This thesis, therefore, has investigated different works of image segmentation algorithms and techniques, including unsupervised and supervised methods. Further, the thesis has developed and implemented two systems of the accurate retinal vessel segmentation. The methodologies explained and analyzed in this thesis, have been selected as the most efficient approaches to achieve higher precision, better robustness, and faster execution speed, to meet the strict standard of the modern medical imaging. Based on the intensive investigation and experiments, this thesis has proposed two outstanding implementations of the retinal blood vessel segmentation. The first implementation focuses on the fast, accurate and robust extraction of the retinal vessels using unsupervised techniques, by applying morphology-based global thresholding to draw the retinal venule structure and centerline detection to extract the capillaries. Besides, this system has been designed to minimize the computing complexity and to process multiple independent procedures in parallel. The second proposed system has especially focused on robustness and accuracy in regardless of execution time. This method has utilized the full convolutional neural network trained from a pre-trained semantic segmentation model, which is also called the transfer deep learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition. Both of the implementations have outperformed their related works and have presented a remarkable scientific value for future computer-aided diagnosis applications. What’s more, this thesis is also a research guide which provide readers with the comprehensive knowledge on how to research on the task of retinal vessel segmentation.
      Degree
      Master of Science (M.Sc.)
      Department
      Electrical and Computer Engineering
      Program
      Electrical Engineering
      Supervisor
      Ko, Seokbum
      Committee
      Chen, Li; Chung, Chi Yung; Chang, Gap Soo
      Copyright Date
      August 2017
      URI
      http://hdl.handle.net/10388/8037
      Subject
      Retinal blood vessel segmentation
      Retinal images
      Deep learning
      Fully convolutional network
      Transfer learning
      Pre-trained model
      Morphological processing
      DoOG filter
      Automated analysis
      Thresholding
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