Computer-Aided Diagnosis of Glaucoma using Morphological Filters and Machine Learning (Primers in Biomedical Imaging Devices and Systems)

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Notă GoodReads:
en Limba Engleză Paperback – June 2023
Computer-Aided Diagnosis of Glaucoma using Morphological Filters and Machine Learning provides a focused research-based case-study of CAD of glaucoma using advanced image processing and machine learning algorithms. The book discusses relevant, state-of-the-art solutions and existing challenges, along with the steps needed to develop a CAD methodology as a projected solution. Different cases of vision disorders specific to Glaucoma are presented with results evaluated by various image quality assessment metrics and opinions from medical practitioners.

  • Highlights advancements in morphological filtering for contrast and edge enhancement of retinal images followed by optic cup/disc segmentation
  • Features simulation results on more than 30 cases of Glaucoma with varying abnormalities and severities
  • Provides remedial solutions of machine learning and a range of novel solutions in the domain of biomedical imaging for CAD in Glaucoma
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ISBN-13: 9780323954129
ISBN-10: 032395412X
Pagini: 250
Dimensiuni: 152 x 229 mm
Seria Primers in Biomedical Imaging Devices and Systems

Public țintă

Professionals, researchers, and graduate students working in the field of Medical Imaging, Biomedical Optics, Image Processing, Computer Vision, Healthcare Management and Intelligent Systems. Also practicing Ophthalmologists, clinicians, post-doc and PhD researchers.
Practicing Ophthalmologists, clinicians


1. Introduction
2. Cad of glaucoma
3. Evolution in cad of glaucoma
4. State of art techniques for cad of glaucoma
5. Design methodology for cad of glaucoma
6. Enhancement of retinal fundus images using morphological filters
7. Segmentation and localization of enhanced retinal images
8. Results and discussions: Enhancement and segmentation of retinal images
9. Feature extraction and classification for automated glaucoma diagnosis
10. Results and discussions: Feature extraction and classification
11. Conclusions and future research