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An Automatic Graph Based Approach for Artery/Vein
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An Automatic Graph-Based Approach for Artery/Vein

Category : Image Processing


Sub Category : BIOMEDICAL


Project Code : IMP22


Project Abstract

The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features.

 

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:

          This system classifies separately the vessels detected in each quadrant, and finally it combines the results.

          Tracking strategy based on a minimal path approach is applied to join the vessel segments located at different radii in order to support the classification by voting.

PROPOSED CONCEPT:       

          We mostly focus on a characteristic of the retinal vessel tree that, at least in the region near the optic disc, veins rarely cross veins and arteries rarely cross arteries.

          We may define different types of intersection points: bifurcation, crossing, meeting, and connecting points.

EXISTING ALGORITHM:

          Colour based Clustering Algorithm

PROPOSED ALGORITHM:

          Automatic Graph Generation Algorithm          

ALGORITHM DEFINITION:

          The clustering approach divides the retinal image into four quadrants.

ALGORITHM DEFINITION:

          The graph extracted from the segmented retinal Vasculature is analyzed to decide on the type of intersection points (graph nodes), and afterwards one of two labels is assigned to each vessel segment (graph links).

DRAWBACKS:

          The classification is propagated outside this zone, where little or no information is available to discriminate arteries from veins.

          Low accuracy.

 

ADVANTAGES:

          Arteries and veins information extracted from a graph which represents the vascular network.

          High accuracy.

 


 
 
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