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

fYear

2014

Firstpage

1518

Lastpage

1521

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);

fLanguage

English

Publisher

ieee

Conference_Titel

Circuit, Power and Computing Technologies (ICCPCT), 2014 International Conference on

Print_ISBN

978-1-4799-2395-3

Type

conf

DOI

10.1109/ICCPCT.2014.7054987

Filename

7054987