Volume 19, No. 5, 2022
K-Means Clustering And Two-Level Classification For Vessel Segmentation In Detection Of Diabetic Retinopathy
Dr. Sudhir W. Mohod , Malpe Kalpana Devidas
Abstract
A frequent and sometimes sight-threatening consequence of diabetes is diabetic retinopathy. In order to diagnose and track diabetic retinopathy, retinal blood vessels must be accurately identified early on and segmented. The improvements in vessel segmentation methods for the identification of diabetic retinopathy are summarised in this abstract. Due to the intricate structure and diversity of blood vessels, as well as the presence of noise and artefacts, vascular segmentation in retinal pictures is a difficult process. In order to overcome these difficulties and increase the precision of vessel segmentation, a number of approaches have been developed. Feature extraction, classification, post-processing, and picture pre-processing are frequently combined in these methods. Recent research have demonstrated encouraging outcomes in vascular segmentation using a suggested framework utilising hybrid models. To extract pertinent information from retinal pictures, the framework uses pre-processing techniques such cropping, colour space conversion, and contrast augmentation. Gabor filtering and texture analysis are two techniques for feature extraction that effectively capture specific vessel properties. Vessel and non-vessel pixels are distinguished using classification techniques like K-means clustering and ensemble classifiers. The vessel segmentation findings are refined using post-processing techniques including morphological operations and linked component analysis. The STARE dataset and other benchmark datasets used for this approach evaluation showed great accuracy, specificity, and sensitivity. However, there are still issues with establishing high specificity and good sensitivity in vessel segmentation. More investigation is required to enhance vascular abnormality identification and lessen false-positive and false-negative mistakes. The improvements in vascular segmentation techniques help to identify and monitor diabetic retinopathy early, allowing for prompt therapies to avert vision loss. The suggested framework and hybrid models have the potential to improve vessel segmentation's precision and effectiveness. The integration of deep learning methodologies and the creation of reliable, automated systems for healthcare applications may be future research priorities.
Pages: 826-840
Keywords: diabetic retinopathy, vessel segmentation, retinal blood vessels, image analysis, pre-processing, feature extraction, classification, post-processing, hybrid models.