Photo Acoustic and Optical Coherence Tomography Imaging, Volume 2: Fundus Imaging for the Retina

238 Pages·2022·81.475 MB·English

by  Aymal El-Baz, Jasjit S. Suri

Photo Acoustic and Optical
Coherence Tomography Imaging,
Volume 2
Fundus imaging for the retina
Online at: https://doi.org/10.1088/978-0-7503-2056-6

Photo Acoustic and Optical
Coherence Tomography Imaging,
Volume 2
Fundus imaging for the retina
Edited by
Ayman El-Baz
University of Louisville, Louisville, KY, USA
and
University of Louisville at Alamein International University (UofL-AIU),
New Alamein City, Egypt
Jasjit S Suri
AtheroPoint LLC, Roseville, CA, USA
IOP Publishing, Bristol, UK
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ISBN 978-0-7503-2056-6(ebook)
ISBN 978-0-7503-2054-2(print)
ISBN 978-0-7503-2055-9(myPrint)
ISBN 978-0-7503-2057-3(mobi)
DOI 10.1088/978-0-7503-2056-6
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Withloveandaffectiontomymotherandfather,whoselovingspiritsustainsmestill.
—Ayman El-Baz
To my late loving parents, immediate family, and children.
—Jasjit S Suri

Contents
Preface xii
Acknowledgements xiii
Editor biographies xiv
List of contributors xv
1 Texture interpretability of fundus imaging and diabetic 1-1
retinopathy: a review
Ahmed Naglah, Mohammed Ghazal, Fatma Taher, Ali Mahmoud
and Ayman El-Baz
1.1 Introduction 1-1
1.2 DR interpretability approaches 1-2
1.2.1 Attention based models 1-2
1.2.2 Feed-forward style localization maps 1-4
1.2.3 Occlusion based models 1-4
1.2.4 Hand-crafted texture analysis methods 1-5
1.2.5 Other approaches 1-5
1.3 Texture challenges in a fundus imaging dataset 1-6
1.3.1 Intensity based analysis is challenging for DR classification 1-6
1.3.2 Image gradient analysis is challenging for DR classification 1-7
1.4 Detecting texture patterns with the aid of deep learning 1-7
1.4.1 Convolutional neural network model 1-14
1.4.2 Kernel clustering 1-15
1.4.3 Visualization 1-15
1.5 Limitations and challenges 1-22
1.6 Conclusion 1-22
References 1-22
2 A two-phase novel optic disc detection algorithm based on 2-1
vesselness distribution and a fuzzy classifier using
vessel ramification and fundus color features
Mehmet Nergiz
2.1 Introduction 2-1
2.2 Materials and methods 2-4
2.2.1 Materials 2-4
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PhotoAcousticandOpticalCoherenceTomographyImaging,Volume2
2.2.2 Methods 2-5
2.3 Evaluation and results 2-24
2.4 Discussion and conclusion 2-26
References 2-32
3 Application of enface image registration/alignment to 3-1
introduce new ocular imaging biomarkers
Zahra Amini, Rahele Kafieh, Mahnoosh Tajmirriahi,
Zeinab Parsons and Hossein Rabbani
3.1 Introduction 3-1
3.1.1 Anatomic landmarks of the eye 3-2
3.1.2 Enface image modalities 3-3
3.2 Feature extraction 3-4
3.2.1 Vessel segmentation 3-4
3.2.2 Optic disc segmentation 3-6
3.2.3 FAZ segmentation 3-7
3.3 Registration of fundus and OCT images 3-7
3.4 Registration and alignment of ocular images in the right and 3-9
left eyes
3.4.1 Symmetry analysis of vessel maps in the right and left eyes 3-9
3.4.2 Symmetry analysis of macular OCTs in the right and 3-10
left eyes
3.4.3 Alignment of ONH OCTs in the right and left eyes 3-12
3.5 Registration of FFA and SLO images 3-12
3.5.1 FFA and OCT registration using SLO images 3-12
3.5.2 MA detection in OCT and FFA 3-15
3.6 Conclusion 3-20
References 3-21
4 Existing techniques used for retinal image analysis in the 4-1
automated detection and prediction of AMD
Samina Khalid, Usman Akram, Tehmina Sheryar and Farah Karim
4.1 Introduction 4-1
4.2 Existing automated diagnosis methods used for AMD 4-4
4.2.1 Pre-processing techniques 4-4
4.2.2 Retinal component extraction from fundoscopic images 4-4
viii
PhotoAcousticandOpticalCoherenceTomographyImaging,Volume2
4.3 Retinal component extraction from OCT images 4-10
4.3.1 Retinal layer extraction 4-11
4.3.2 Drusen detection from extracted retinal layers 4-12
4.3.3 AMD lesion detection 4-14
4.4 Comparative analysis of diagnosis systems 4-16
4.5 Research limitations and challenges 4-17
4.6 Conclusion 4-21
References 4-22
5 Interobserver variability in the determination of diabetic 5-1
retinopathy and quality of fundus image
S´ılvia Reˆgo, Matilde Monteiro-Soares, Filipe Soares
and Marco Dutra-Medeiros
5.1 Introduction 5-1
5.2 Fundus imaging 5-2
5.2.1 The diabetic retinopathy classification process 5-2
5.2.2 Fundus image quality 5-3
5.3 Interobserver variability and reliability of DR classification 5-4
5.4 Image quality 5-5
5.5 Implications for clinical practice 5-6
5.6 Conclusion 5-7
References 5-7
6 Computer-aided diagnosis of Plus disease in preterm infants 6-1
Sivakumar Ramachandran, Bejoy Abraham, P Niyas, Anand Vinekar
and Renu John
6.1 Introduction 6-1
6.1.1 Retinal image analysis 6-1
6.2 Computer-aided diagnosis system 6-3
6.2.1 Resources and evaluation metrics 6-4
6.3 Development of a computer-aided diagnostic system 6-6
6.3.1 Tortuosity detection and quantification using a fully 6-7
convolutional neural network
6.4 Results and discussion 6-11
6.5 Conclusions 6-13
References 6-13
ix