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Hybrid Feature Analysis for Assessment of Glaucoma Using RNFL Defects
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Abstract
Retinal Nerve Fiber Layer (RNFL) evaluation is important for diagnosis of glaucoma in the earlier stage as RNFL changes precedes visual field loss and optic disc changes. An early detection of changes in the texture caused by nerve fibers is important in the diagnosis of glaucoma. Red free fundus image are preprocessed, and region of interest is cropped in the inferior and in the superior region of the fundus image around the optic disc and regional profile analysis is performed to identify the RNFL defect region. Moment based intensity features, Wavelet based second order textural features using Gray Level Co-occurrence Matrix (GLCM) and wavelet based energy features are extracted from the segmented images. Extracted features are fed as input to Adaptive Neuro Fuzzy Inference System to classify the images as normal or glaucomatous eye. The method achieves 98.3% sensitivity, 96% specificity and 97.23% accuracy and can be used as a decision support system for clinical diagnosis
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References
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