Title :
Segmentation and classification of anomaly in fundus images
Author :
Srilakshmi, E.K. ; Vasanthi, S.
Author_Institution :
Dept. of ECE, KSR Coll. of Technol., Namakkal, India
Abstract :
Automatic detection of microaneurysms (MAs) is proposed in this paper. The detection of MAs is essential step in the diagnosis and grading of diabetic retinopathy. Microaneurysms appear as a small round shaped dots on the retina. Here MA is detected by the method of cross-sectional profile analysis. Naive baye´s classifier is used for classifying detected components as microaneurysm and non-microaneurysm components. Performance of this method is calculated by using microaneurysm score calculation.
Keywords :
diseases; eye; image classification; image segmentation; medical image processing; patient diagnosis; Naive Baye classifier; anomaly classification; anomaly segmentation; cross-sectional profile analysis; diabetic retinopathy diagnosis; diabetic retinopathy grading; fundus images; microaneurysm automatic detection; microaneurysm score calculation; nonmicroaneurysm component; retina; Biomedical imaging; Blood vessels; Computers; Feature extraction; Image segmentation; Retina; Standards; Biomedical image processing; Diabetic Retinopathy (DR); Naive Bayes classifier (NB);
Conference_Titel :
Circuit, Power and Computing Technologies (ICCPCT), 2014 International Conference on
Print_ISBN :
978-1-4799-2395-3
DOI :
10.1109/ICCPCT.2014.7054987
