title: Wavelet-based energy features for glaucomatous image classification.
abstract: 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.
authors: Computer Science Program, Louisiana Tech University, Ruston, LA 71272, USA. sdua@coes.latech.edu
authors: Sumeet
authors: S
authors: Dua
authors: U Rajendra
authors: UR
authors: Acharya
authors: Pradeep
authors: P
authors: Chowriappa
authors: S Vinitha
authors: SV
authors: Sree
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001499:Bayes Theorem; D003941:Diagnostic Techniques, Ophthalmological; D005901:Glaucoma / Q000175:diagnosis* / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D015203:Reproducibility of Results; D058067:Wavelet Analysis