Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/102630
Title: Wavelet-based energy features for glaucomatous image classification
Authors: Dua, Sumeet
Acharya, U. Rajendra
Chowriappa, Pradeep
Sree, Subbhuraam Vinitha
Keywords: DRNTU::Engineering::Mechanical engineering::Bio-mechatronics
Issue Date: 2011
Source: Dua, S., Acharya, U. R., Chowriappa, P., & Sree, S. V. (2012). Wavelet-based energy features for glaucomatous image classification. IEEE transactions on information technology in biomedicine, 16(1), 80-87.
Series/Report no.: IEEE transactions on information technology in biomedicine
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.
URI: https://hdl.handle.net/10356/102630
http://hdl.handle.net/10220/16461
DOI: http://dx.doi.org/10.1109/TITB.2011.2176540
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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