Retinal Image Analysis for Abnormality Detection-An Overview | Science Publications

Journal of Computer Science

R. Karthikeyan¹  P. Alli¹ 

关键词: Classification;    diabetic retinopathy;    retinal disorder;    NPDR;    CRVO;    CNVM;    CSR;    Cellular Neural Network (CNN);    diabetic retinopathy;   

DOI  :  10.3844/jcssp.2012.436.442

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

Problem statement: Classification plays a major role in retinal image analysis for detecting the various abnormalities in retinal images. Classification refers to one of the mining concepts using supervised or unsupervised learning techniques. Approach: Diabetic retinopathy is one of the common complications of diabetes. Unfortunately, in many cases, the patient is not aware of any symptoms until it is too late for effective treatment. Diabetic retinopathy is the leading cause of blindness. Diabetic retinopathy results in retinal disorders that include microaneursyms, drusens, hard exudates and intra-retinal micro-vascular abnormalities. Results: Automatic methods to detect various lesions associated with diabetic retinopathy facilitate the opthalmologists in accurate diagnosis and treatment planning. Abnormal retinal images fall into four different classes namely Non-Proliferative Diabetic Retinopathy (NPDR), Central Retinal Vein Occlusion (CRVO), Choroidal Neo-Vascularization Membrane (CNVM) and Central Serous Retinopathy (CSR).. Conclusion: In this study, we have analysed the various methodologies for detecting the abnormalities in retinal images automatically along with their merits and demerits and proposed the new framework for detection of abnormalities using Cellular Neural Network (CNN).

【 授权许可】

Unknown