Diabetes and Fundus OCT Computer-Assisted Diagnosis Series
Coordonnateurs : S. El-Baz Ayman, Suri Jasjit
Diabetes and Fundus OCT brings together a stellar cast of authors who review the computer-aided diagnostic (CAD) systems developed to diagnose non-proliferative diabetic retinopathy in an automated fashion using Fundus and OCTA images. Academic researchers, bioengineers, new investigators and students interested in diabetes and retinopathy need an authoritative reference to bring this multidisciplinary field together to help reduce the amount of time spent on source-searching and instead focus on actual research and the clinical application. This reference depicts the current clinical understanding of diabetic retinopathy, along with the many scientific advances in understanding this condition.
As the role of optical coherence tomography (OCT) in the assessment and management of diabetic retinopathy has become significant in understanding the vireo retinal relationships and the internal architecture of the retina, this information is more critical than ever.
1. Computer Aided Diagnosis System Based on a Comprehensive Local Features Analysis for Early Diabetic Retinopathy Detection using OCTA
2. Deep Learning Approach for Classification of Eye Diseases Based on Color Fundus Images
3. Fundus Retinal Image Analyses for Screening and Diagnosing Diabetic Retinopathy, Macular edema, and Glaucoma Disorders
4. Mobile Phone Based Diabetic Retinopathy Detection System
5. Computer Aided Diagnosis of Age Related Macular Degeneration by OCT, Fundus Image Analysis
6. Retinal Diseases Diagnosis Based on Optical Coherence Tomography Angiography (OCTA)
7. Optical Coherence Tomography: A Review
8. An Accountable Saliency-Oriented Data-Driven Approach to Diabetic Retinopathy Detection
9. Machine Learning Based Abnormalities Detection In Retinal Fundus Images
10. Optical Coherence Tomography Angiography of Retinal Vascular Diseases
11. Screening of The Diabetic Retinopathy In Engineering
12. Optical Coherence Tomography Angiography In Type 3 Neovascularization
13. Diabetic Retinopathy Detection in Ocular Images by Dictionary Learning
14. Lesion Detection Using Segmented Structure Of Retina
Dr. Jasjit Suri, PhD, MBA, is an innovator, visionary, scientist, and internationally known world leader. Dr Suri received the Director General’s Gold medal in 1980 and Fellow of (i) American Institute of Medical and Biological Engineering, awarded by the National Academy of Sciences, Washington DC, (ii) Institute of Electrical and Electronics Engineers, (iii) American Institute of Ultrasound in Medicine, (iv) Society of Vascular Medicine, (v) Asia Pacific Vascular Society, and (vi) Asia Association of Artificial Intelligence. Dr. Suri was honored with life time achievement awards by Marcus, NJ, USA and Graphics Era University, Dehradun, India. He has published nearly 300 peer-reviewed Artificial Intelligence articles, nearly 2000 Google Scholar Publications, 100 books, and 100 innovations/trademarks leading to an H-index of nearly 100 with about 43,000 citations. He has held positions as chairman of AtheroPoint, CA, USA, IEEE Denver section, Colorado, USA, and advisory board member to healthcare industries and several universities in the United States of America and abroad.
- Includes unique information for academic clinicians, researchers and bioengineers
- Provides insights needed to understand the imaging modalities involved, the unmet clinical need that is being addressed, and the engineering and technical approaches applied
- Brings together details on the retinal vasculature in diabetics as imaged by optical coherence tomography angiography and automated detection of retinal disease
Date de parution : 04-2020
Ouvrage de 434 p.
19x23.3 cm
Thème de Diabetes and Fundus OCT :
Mots-clés :
Diabetes; optical coherence tomography; imaging; diabetic retinopathy; optical coherence tomography; angiography; capillary nonperfusion; vascular perfusion density mapping; image contrast enhancement technique; diagnostic tools for diabetic retinopathy; red lesion detection fundus images