Retinal Image Classification for Identification of Cardio Vascular Disease Using Forest Graph
|International Journal of Computer Science and Engineering|
|© 2014 by SSRG - IJCSE Journal|
|Volume 1 Issue 8|
|Year of Publication : 2014|
|Authors : A.Rijuvana Begum, S.Purnima|
How to Cite?
A.Rijuvana Begum, S.Purnima, "Retinal Image Classification for Identification of Cardio Vascular Disease Using Forest Graph," SSRG International Journal of Computer Science and Engineering , vol. 1, no. 8, pp. 1-5, 2014. Crossref, https://doi.org/10.14445/23488387/IJCSE-V1I8P102
The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as hypertension, coronary heart disease, and stroke has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. Accurate measurement of vessel diameters on retinal images plays an important part in diagnosing cardiovascular diseases. In this project, a method of vessel diameter measurement has been developed incorporating with a tracking technique. Vessel edges are then more precisely localized using image profiles computed perpendicularly across a spline fit of each detected vessel centerline, so that both local and global changes in vessel diameter can be readily quantified. The retinal vessel network is used to diagnosis of cardiovascular disease. We use the post processing step to identifying the true vessels from for vascular structure segmentation. So we construct the vessel segment graph and formulate the problem of finding optimal forest in the graph. Using image datasets, we show that the diameters output by our algorithm display good agreement with the manual measurements made by independent observers.
VESSEL SEGMENTATION, GRAPH, VASCULAR STRUCTURE SEGMENTATION.
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