Decision support system for age-related macular degeneration using discrete wavelet transform.
Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; h...
| Published in: | Medical & Biological Engineering & Computing Vol. 52; no. 9; pp. 781 - 797 |
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| Main Authors: | , , , , , , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Sep2014
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103840783&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103840783 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2014 vid: 52 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103840783 NLM25112273 2012689110 10.1007/s11517-014-1180-8 NLM25112273 103840783 ppf: 781 ppct: 16 formats: fmt: @attributes: type: P tig: atl: Decision support system for age-related macular degeneration using discrete wavelet transform. aug: au: Mookiah, Muthu Rama Krishnan Acharya, U Rajendra Koh, Joel E W Chua, Chua Kuang Tan, Jen Hong Chandran, Vinod Lim, Choo Min Noronha, Kevin Laude, Augustinus Tong, Louis affil: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, 599489, Singapore, mkm2@np.edu.sg. sug: subj: Expert Systems Equipment and Supplies Macular Degeneration Diagnosis Software Signal Processing, Computer Assisted Aged Aged, 80 and Over Probability Retina Human Image Processing, Computer Assisted Middle Age ROC Curve Sensitivity and Specificity Algorithms Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years ab: Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, [Formula: see text]-nearest neighbor ([Formula: see text]-NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70%, sensitivity of 91.11%, and specificity of 96.30% using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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