Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image
Language English Country Ireland Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
26574297
DOI
10.1016/j.cmpb.2015.10.010
PII: S0169-2607(15)00270-9
Knihovny.cz E-resources
- Keywords
- Blood vessels, Classification, Feature extraction, Fundus image, Glaucoma, Wavelet transform,
- MeSH
- Algorithms * MeSH
- Optic Disk pathology MeSH
- Adult MeSH
- Glaucoma pathology MeSH
- Image Interpretation, Computer-Assisted methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Reproducibility of Results MeSH
- Retinoscopy methods MeSH
- Pattern Recognition, Automated methods MeSH
- Aged MeSH
- Sensitivity and Specificity MeSH
- Machine Learning MeSH
- Subtraction Technique MeSH
- Wavelet Analysis MeSH
- Image Enhancement methods MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification.
Brno University of Technology Faculty of Electrical Engineering Czech Republic
Department of Electronics and Communication Engineering Amity University Noida Uttar Pradesh India
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