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.
1. Chapter 1: A brief introduction and a glimpse into the past - p. xxii 2. Chapter 2: Clinical motivation and the needs for RIA in healthcare - p. 4 3. Chapter 3: The physics, instruments and modalities of retinal imaging - p. 17 4. Chapter 4: Retinal image preprocessing, enhancement, and registration - p. 57 5. Chapter 5: Automatic landmark detection in fundus photography - p. 77 6. Chapter 6: Retinal vascular analysis: Segmentation, tracing, and beyond - p. 93 7. Chapter 7: OCT layer segmentation - p. 120 8. Chapter 8: Image quality assessment - p. 133 9. Chapter 9: Validation - p. 155 10. Chapter 10: Statistical analysis and design in ophthalmology: Toward optimizing your data - p. 170 11. Chapter 11: Structure-preserving guided retinal image filtering for optic disc analysis - p. 197 12. Chapter 12: Diabetic retinopathy and maculopathy lesions - p. 221 13. Chapter 13: Drusen and macular degeneration - p. 243 14. Chapter 14: OCT fluid detection and quantification - p. 272 15. Chapter 15: Retinal biomarkers and cardiovascular disease: A clinical perspective - p. 298 16. Chapter 16: Vascular biomarkers for diabetes and diabetic retinopathy screening - p. 318 17. Chapter 17: Image analysis tools for assessment of atrophic macular diseases - p. 352 18. Chapter 18: Artificial intelligence and deep learning in retinal image analysis - p. 378 19. Chapter 19: AI and retinal image analysis at Baidu - p. 404 20. Chapter 20: The challenges of assembling, maintaining and making available large data sets of clinical data for research - p. 427 21. Chapter 21: Technical and clinical challenges of A.I. in retinal image analysis - p. 444 22. Index - p. 467
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.
1. Chapter 1: A brief introduction and a glimpse into the past - p. xxii 2. Chapter 2: Clinical motivation and the needs for RIA in healthcare - p. 4 3. Chapter 3: The physics, instruments and modalities of retinal imaging - p. 17 4. Chapter 4: Retinal image preprocessing, enhancement, and registration - p. 57 5. Chapter 5: Automatic landmark detection in fundus photography - p. 77 6. Chapter 6: Retinal vascular analysis: Segmentation, tracing, and beyond - p. 93 7. Chapter 7: OCT layer segmentation - p. 120 8. Chapter 8: Image quality assessment - p. 133 9. Chapter 9: Validation - p. 155 10. Chapter 10: Statistical analysis and design in ophthalmology: Toward optimizing your data - p. 170 11. Chapter 11: Structure-preserving guided retinal image filtering for optic disc analysis - p. 197 12. Chapter 12: Diabetic retinopathy and maculopathy lesions - p. 221 13. Chapter 13: Drusen and macular degeneration - p. 243 14. Chapter 14: OCT fluid detection and quantification - p. 272 15. Chapter 15: Retinal biomarkers and cardiovascular disease: A clinical perspective - p. 298 16. Chapter 16: Vascular biomarkers for diabetes and diabetic retinopathy screening - p. 318 17. Chapter 17: Image analysis tools for assessment of atrophic macular diseases - p. 352 18. Chapter 18: Artificial intelligence and deep learning in retinal image analysis - p. 378 19. Chapter 19: AI and retinal image analysis at Baidu - p. 404 20. Chapter 20: The challenges of assembling, maintaining and making available large data sets of clinical data for research - p. 427 21. Chapter 21: Technical and clinical challenges of A.I. in retinal image analysis - p. 444 22. Index - p. 467
