Wavelet-based energy features for glaucomatous image classification

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 daub...

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Main Authors: Dua, Sumeet, Acharya, U. Rajendra, Chowriappa, Pradeep, Sree, Subbhuraam Vinitha
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/102630
http://hdl.handle.net/10220/16461
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1026302020-03-07T13:22:20Z Wavelet-based energy features for glaucomatous image classification Dua, Sumeet Acharya, U. Rajendra Chowriappa, Pradeep Sree, Subbhuraam Vinitha School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering::Bio-mechatronics 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. 2013-10-11T04:50:51Z 2019-12-06T20:57:53Z 2013-10-11T04:50:51Z 2019-12-06T20:57:53Z 2011 2011 Journal Article 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. https://hdl.handle.net/10356/102630 http://hdl.handle.net/10220/16461 10.1109/TITB.2011.2176540 en IEEE transactions on information technology in biomedicine
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Mechanical engineering::Bio-mechatronics
spellingShingle DRNTU::Engineering::Mechanical engineering::Bio-mechatronics
Dua, Sumeet
Acharya, U. Rajendra
Chowriappa, Pradeep
Sree, Subbhuraam Vinitha
Wavelet-based energy features for glaucomatous image classification
description 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.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Dua, Sumeet
Acharya, U. Rajendra
Chowriappa, Pradeep
Sree, Subbhuraam Vinitha
format Article
author Dua, Sumeet
Acharya, U. Rajendra
Chowriappa, Pradeep
Sree, Subbhuraam Vinitha
author_sort Dua, Sumeet
title Wavelet-based energy features for glaucomatous image classification
title_short Wavelet-based energy features for glaucomatous image classification
title_full Wavelet-based energy features for glaucomatous image classification
title_fullStr Wavelet-based energy features for glaucomatous image classification
title_full_unstemmed Wavelet-based energy features for glaucomatous image classification
title_sort wavelet-based energy features for glaucomatous image classification
publishDate 2013
url https://hdl.handle.net/10356/102630
http://hdl.handle.net/10220/16461
_version_ 1681046960172171264