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.
Retinal thickness maps obtained using a scanning laser ophthalmoscope are useful in the diagnosis of macular edema and
other diseases that cause changes in the retinal thickness. However, the thickness measurements are adversely affected
by the presence of blood vessels. This paper studies the effect that the blood vessels have on the computation of the
retinal thickness. The retinal thickness is estimated using maximum-likelihood resolution with anatomical constraints.
The blood vessels are segmented using local image features. Comparison of the retinal thickness with and without the
blood vessel removal is made using correlation coefficient and I-divergence.
A multistage algorithm is presented, whose components are based upon maximum likelihood estimation (MLE). From
3D scanning laser ophthalmoscope (SLO) image data, the algorithm finds the positions of the two anatomical boundaries
of the eye's fundus that define the retina, which are the internal limiting membrane (ILM) and the retinal pigment
epithelium (RPE). he retinal thickness is then calculated by subtraction. Retinal thickness is useful for indicating,
assessing risk of, and following several diseases, including various forms of macular edema and cysts.