Computational Retinal Image Analysis: Tools, Applications and Perspectives

Computational Retinal Image Analysis: Tools, Applications and Perspectives
PDF
  • eBook:
    Computational Retinal Image Analysis: Tools, Applications and Perspectives
  • Author:
    Emanuele Trucco, Tom MacGillivray, Yanwu Xu
  • Edition:
    1 edition
  • Categories:
  • Data:
    December 4, 2019
  • ISBN:
    0081028164
  • ISBN-13:
    9780081028162
  • Language:
    English
  • Pages:
    502 pages
  • Format:
    PDF

Book Description
Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more.

Content

CHAPTER 1. A brief introduction and a glimpse into the past
CHAPTER 2. Clinical motivation and the needs for RIA in healthcare
CHAPTER 3. The physics, instruments and modalities of retinal imaging
CHAPTER 4. Retinal image preprocessing, enhancement, and registration
CHAPTER 5. Automatic landmark detection in fundus photography
CHAPTER 6. Retinal vascular analysis: Segmentation, tracing, and beyond
CHAPTER 7. OCT layer segmentation
CHAPTER 8. Image quality assessment
CHAPTER 9. Validation
CHAPTER 10. Statistics in ophthalmology
CHAPTER 11. Structure-preserving guided retinal image filtering for optic disc analysis
CHAPTER 12. Diabetic retinopathy and maculopathy lesions
CHAPTER 13. Drusen and macular degeneration
CHAPTER 14. OCT fluid detection and quantification
CHAPTER 15. Retinal biomarkers and cardiovascular disease: A clinical perspective
CHAPTER 16. Vascular biomarkers for diabetes and diabetic retinopathy screening
CHAPTER 17. Image analysis tools for assessment of atrophic macular diseases
CHAPTER 18. Artificial intelligence and deep learning in retinal image analysis
CHAPTER 19. AI and retinal image analysis at Baidu
CHAPTER 20. The challenges of assembling, maintaining and making available large data sets of clinical data for research
CHAPTER 21. Technical and clinical challenges of A.I. in retinal image analysis

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