AutomaticDetectionofBloodVesselsinDigitalRetinalImage创新.pptVIP

Automatic Detection of Blood Vessels  in Digital Retinal Image using  CVIP Tools Krishna Praveena Mandava Sri Swetha Kantamaneni Robert LeAnder Overview The Devastation Diabetic retinopathy – 4.1 million US Adults National Health Interview Survey and US Census Population Glaucoma – 2 million individuals in the US. Ophthalmologic images Important structures – Blood Vessels Help detect and treat Eye Diseases affecting blood vessels Overview Damaged blood vessels indicate retinal disease. Blood clots indicate diabetic retinopathy. Narrow blood vessels indicate Central Retinal Artery Occlusion. Observation of blood vessels in retinal images Shows presence of disease Helps prevent vision loss by early detection The Need for the Study  Automated Blood Vessel Extraction algorithms can save time, patients’ vision and medical costs. Effects of Diseases on Blood Vessels Image of Diseased Retina Due to Diabetes Central Retinal Artery Occlusion (CRAO) Branch Retinal Artery Occlusion (BRAO)  6 Approaches to Blood Vessel Extraction Pattern recognition techniques Model based approaches Tracking based approaches Artificial intelligence based approaches Neural network based approaches Miscellaneous tube-like object detection approaches. Methods Steps used blood vessel extraction… Preprocessing Extraction (segmentation) Post processing Preprocessing: Preprocessing will eliminate errors caused during taking the image and to reduce brightness effects on the image . The original images are resized from 150*130 to 256*256 to use in CVIP tools. Images in green bands show vessel structures most reliably. So, the green band was extracted. Extraction of blood vessels: Tools that we applied: Median filters Laplacian filters Image enhancement methods like Adaptive Contrast Enhancement, Histogram equalization. Edge detection like Canny edge detection. Post processing: The output images from blood vessel extraction were processed