Retinal Imaging and Image Analysis
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TLDR
Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed and aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.Abstract:
Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.read more
Citations
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PixelBNN: Augmenting the PixelCNN with batch normalization and the presentation of a fast architecture for retinal vessel segmentation
TL;DR: PixelBNN as discussed by the authors is a state-of-the-art method for segmentation of fundus morphologies, which is trained, tested and cross tested on the DRIVE, STARE and CHASE\_DB1 retinal vessel segmentation datasets.
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A Review on Glaucoma Disease Detection Using Computerized Techniques
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Left-invariant evolutions of wavelet transforms on the similitude group
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Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface
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Morphological operations with iterative rotation of structuring elements for segmentation of retinal vessel structures
TL;DR: A hysteresis thresholding, guided by some morphological operations has been employed to obtain the binary image excluding other unwanted areas of the blood vessel from its background, and a maximum accuracy and an average accuracy of 95.65 and 94.31% respectively have been achieved.
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Optical coherence tomography
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