Wavelet-Based Energy Features for Glaucomatous Image Classification

• Published in: IEEE transactions on information technology in biomedicine Vol. 16; no. 1; pp. 80 - 87
• Main Authors: Dua, S., Acharya, U. R., Chowriappa, P., Sree, S. V.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2012
• Subjects: Adult; Aged; Algorithms; Bayes Theorem; Biomedical optical imaging; data mining; Diagnostic Techniques, Ophthalmological; Discrete wavelet transforms; Feature extraction; glaucoma; Glaucoma - diagnosis; Glaucoma - pathology; Humans; Image Interpretation, Computer-Assisted - methods; image texture; Matrix decomposition; Middle Aged; Reproducibility of Results; Retina; Support vector machines; Wavelet Analysis; wavelet transforms
• ISSN: 1089-7771; 1558-0032
• DOI: 10.1109/TITB.2011.2176540

Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naïve Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.