Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Kirar, Bhupendra Singh; Agrawal, Dheeraj Kumar
• Is Part Of: IET image processing. Volume 13:Issue 1(2019)
• Page Start: 73
• Page End: 82
• Publication Date: 2019-01-01
• Subjects: singular value decomposition -- discrete wavelet transforms -- diseases -- wavelet transforms -- biomedical optical imaging -- support vector machines -- eye -- image classification -- feature extraction -- medical image processing; singular value decomposition -- fourteen robust features -- glaucoma detection -- discrete wavelet -- fundus images -- eye disorder -- progressive deterioration -- optic nerve fibres -- empirical wavelet transforms -- EWT -- feature extraction -- image decomposition -- hybrid concatenation approach -- DWT -- band images -- extracted features -- concatenated features; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5297
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Glaucoma is a class of eye disorder; it causes progressive deterioration of optic nerve fibres. Discrete wavelet transforms (DWTs) and empirical wavelet transforms (EWTs) are widely used methods in the literature for feature extraction using image decomposition. However, to increase the accuracy for measuring features of images a hybrid and concatenation approach has been presented in the proposed research work. DWT decomposes images into approximate and detail coefficients and EWT decomposes images into its sub band images. The concatenation approach employs the combination of all features obtained using DWT and EWT and their combination. Extracted features from each of DWT, EWT, DWTEWT and EWTDWT are concatenated. Concatenated features are normalised, ranked and fed to singular value decomposition to find robust features. Fourteen robust features are used by support vector machine classifier. The obtained accuracy, sensitivity and specificity are 83.57, 86.40 and 80.80%, respectively, for tenfold cross validation which outperforms the existing methods of glaucoma detection.