A Multi-Orientation Analysis Approach to Retinal Vessel Tracking
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TLDR
The method runs fully automatically and provides a detailed model of the retinal vasculature, which is crucial as a sound basis for further quantitative analysis of the retina, especially in screening applications.Abstract:
This paper presents a method for retinal vasculature extraction based on biologically inspired multi-orientation analysis. We apply multi-orientation analysis via so-called invertible orientation scores, modeling the cortical columns in the visual system of higher mammals. This allows us to generically deal with many hitherto complex problems inherent to vessel tracking, such as crossings, bifurcations, parallel vessels, vessels of varying widths and vessels with high curvature. Our approach applies tracking in invertible orientation scores via a novel geometrical principle for curve optimization in the Euclidean motion group SE(2). The method runs fully automatically and provides a detailed model of the retinal vasculature, which is crucial as a sound basis for further quantitative analysis of the retina, especially in screening applications.read more
Citations
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Journal ArticleDOI
Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores
Jiong Zhang,Behdad Dashtbozorg,Erik J. Bekkers,Josien P. W. Pluim,Remco Duits,Bart M. ter Haar Romeny +5 more
TL;DR: The proposed algorithm of applying the LAD filter on orientation scores (LAD-OS) outperforms most of the state-of-the-art methods and is capable of dealing with typically difficult cases like crossings, central arterial reflex, closely parallel and tiny vessels.
Journal ArticleDOI
Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion
Pavle Prentasic,Sven Lončarić +1 more
TL;DR: Having a reliable automated output, such as automated segmentation using convolutional neural networks in combination with other landmark detectors, is an important step in creating automated screening programs for early detection of diabetic retinopathy.
Journal ArticleDOI
Recent Advancements in Retinal Vessel Segmentation
TL;DR: A systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years is carried out and provides an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.
Journal ArticleDOI
Retinal vessel delineation using a brain-inspired wavelet transform and random forest
TL;DR: A supervised retinal vessel segmentation by incorporating vessel filtering and wavelet transform features from orientation scores (OSs), and green intensity achieves a competitive segmentation, and better vessel preservation with less false detections compared with the state-of-the-art methods.
Journal ArticleDOI
Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters
TL;DR: A method for the delineation of blood vessels in retinal images that is effective for vessels of different thickness is proposed and the performance of different selection methods based upon machine learning and information theory is compared.
References
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Muliscale Vessel Enhancement Filtering
TL;DR: The multiscale second order local structure of an image (Hessian) is examined with the purpose of developing a vessel enhancement filter and a vesselness measure is obtained.
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TL;DR: The authors present an efficient architecture to synthesize filters of arbitrary orientations from linear combinations of basis filters, allowing one to adaptively steer a filter to any orientation, and to determine analytically the filter output as a function of orientation.