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|Title:||Wavelet-based energy features for glaucomatous image classification||Authors:||Dua, Sumeet
Acharya, U. Rajendra
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
|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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