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Retinal Vessels

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Multi-level deep supervised networks for retinal vessel segmentation.

International journal of computer assisted radiology and surgery
PURPOSE: Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation f...

Retinal blood vessel extraction using tunable bandpass filter and fuzzy conditional entropy.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Extraction of blood vessels on retinal images plays a significant role for screening of different opthalmologic diseases. However, accurate extraction of the entire and individual type of vessel silhouette from the noisy im...

Retinal vessel segmentation in colour fundus images using Extreme Learning Machine.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Attributes of the retinal vessel play important role in systemic conditions and ophthalmic diagnosis. In this paper, a supervised method based on Extreme Learning Machine (ELM) is proposed to segment retinal vessel. Firstly, a set of 39-D discriminat...

Accelerating Convolutional Sparse Coding for Curvilinear Structures Segmentation by Refining SCIRD-TS Filter Banks.

IEEE transactions on medical imaging
Deep learning has shown great potential for curvilinear structure (e.g., retinal blood vessels and neurites) segmentation as demonstrated by a recent auto-context regression architecture based on filter banks learned by convolutional sparse coding. H...

Segmenting Retinal Blood Vessels With Deep Neural Networks.

IEEE transactions on medical imaging
The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of patholo...

Retinal vessel segmentation using multi-scale textons derived from keypoints.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This paper presents a retinal vessel segmentation algorithm which uses a texton dictionary to classify vessel/non-vessel pixels. However, in contrast to previous work where filter parameters are learnt from manually labelled image pixels our filter p...

Retinal vessel extraction using Lattice Neural Networks with Dendritic Processing.

Computers in biology and medicine
Retinal images can be used to detect and follow up several important chronic diseases. The classification of retinal images requires an experienced ophthalmologist. This has been a bottleneck to implement routine screenings performed by general physi...

Trainable COSFIRE filters for vessel delineation with application to retinal images.

Medical image analysis
Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute t...

Relationships Between Retinal Vascular Characteristics and Systemic Indicators in Patients With Diabetes Mellitus.

Investigative ophthalmology & visual science
PURPOSE: To develop a deep learning method for vessel segmentation in fundus images, measure retinal vessels, and study the connection between retinal vascular features and systemic indicators in diabetic patients.

Artificial Intelligence Versus Rules-Based Approach for Segmenting NonPerfusion Area in a DRCR Retina Network Optical Coherence Tomography Angiography Dataset.

Investigative ophthalmology & visual science
PURPOSE: Loss of retinal perfusion is associated with both onset and worsening of diabetic retinopathy (DR). Optical coherence tomography angiography is a noninvasive method for measuring the nonperfusion area (NPA) and has promise as a scalable scre...