Thinning of the outer nuclear layer (ONL) is an important pathological feature and possible biomarker of age-related macular degeneration (AMD). The demarcation of the ONL and Henle’s fiber layer (HFL) is visually unattainable with standard optical coherence tomography (OCT) imaging. In this work, we built a volumetric directional OCT prototype which constitutes two optical scanners in the sample arm that synchronously scan the imaging beam on the pupil and retina. The imaging beam’s entry positions and incident angles on the pupil and retina respectively are precisely controlled and optimally maintained to generate sufficient contrast of the HFL over the entire macular volume.
This study presents a new method of visualizing hybridized images of retinal spectral domain optical coherence
tomography (SDOCT) data comprised of varied directional reflectivity. Due to the varying reflectivity of certain retinal
structures relative to angle of incident light, SDOCT images obtained with differing entry positions result in nonequivalent
images of corresponding cellular and extracellular structures, especially within layers containing
photoreceptor components. Harnessing this property, cross-sectional pathologic and non-pathologic macular images
were obtained from multiple pupil entry positions using commercially-available OCT systems, and custom segmentation,
alignment, and hybridization algorithms were developed to chromatically visualize the composite variance of reflectivity
effects. In these images, strong relative reflectivity from any given direction visualizes as relative intensity of its
corresponding color channel. Evident in non-pathologic images was marked enhancement of Henle’s fiber layer (HFL)
visualization and varying reflectivity patterns of the inner limiting membrane (ILM) and photoreceptor inner/outer
segment junctions (IS/OS). Pathologic images displayed similar and additional patterns. Such visualization may allow a
more intuitive understanding of structural and physiologic processes in retinal pathologies.
This paper proposes an algorithm for retinal image registration involving OCT fundus images (OFIs). The first
application of the algorithm is to register OFIs with color fundus photographs; such registration between multimodal
retinal images can help correlate features across imaging modalities, which is important for both clinical and research
purposes. The second application is to perform the montage of several OFIs, which allows us to construct 3D OCT
images over a large field of view out of separate OCT datasets. We use blood vessel ridges as registration features. The
brute force search and an Iterative Closest Point (ICP) algorithm are employed for image pair registration. Global
alignment to minimize the distance between matching pixel pairs is used to obtain the montage of OFIs. Quality of OFIs
is the big limitation factor of the registration algorithm. In the first experiment, the effect of manual OFI enhancement on
registration was evaluated for the affine model on 11 image pairs from diseased eyes. The average root mean square
error (RMSE) decreases from 58 μm to 40 μm. This indicates that the registration algorithm is robust to manual
enhancement. In the second experiment for the montage of OFIs, the algorithm was tested on 6 sets from healthy eyes
and 6 sets from diseased eyes, each set having 8 partially overlapping SD-OCT images. Visual evaluation showed that
the montage performance was acceptable for normal cases, and not good for abnormal cases due to low visibility of
blood vessels. The average RMSE for a typical montage case from a healthy eye is 2.3 pixels (69 μm).
Automatic detection of retinal blood vessels is important to medical diagnoses and imaging. With the development of
imaging technologies, various modals of retinal images are available. Few of currently published algorithms are applied
to multimodal retinal images. Besides, the performance of algorithms with pathologies is expected to be improved. The
purpose of this paper is to propose an automatic Ridge-Branch-Based (RBB) detection algorithm of blood vessel
centerlines and blood vessels for multimodal retinal images (color fundus photographs, fluorescein angiograms, fundus
autofluorescence images, SLO fundus images and OCT fundus images, for example). Ridges, which can be considered
as centerlines of vessel-like patterns, are first extracted. The method uses the connective branching information of image
ridges: if ridge pixels are connected, they are more likely to be in the same class, vessel ridge pixels or non-vessel ridge
pixels. Thanks to the good distinguishing ability of the designed "Segment-Based Ridge Features", the classifier and its
parameters can be easily adapted to multimodal retinal images without ground truth training. We present thorough
experimental results on SLO images, color fundus photograph database and other multimodal retinal images, as well as
comparison between other published algorithms. Results showed that the RBB algorithm achieved a good performance.
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