Energy Feature Extraction Using Wavelet Transform for Glaucomatous Image Classification

Abstract
Wavelet Transforms is a part of large community of mathematical function approximation method, they are being increasing and being deployed in image processing for segmentation, filtering, classification etc. This work is based on image classification with the use of single level Discrete Wavelet Transform (DWT). Wavelets have been employed in many applications of signal processing. Thetexture features within images are extracted for accurate and efficient Glaucoma Classification. Energy is distributed over the wavelet subbands to find these important texture features. The discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and reverse biorthogonal (rbio3.3, rbio3.5, and rbio3.7) wavelet filters. We propose a technique to extract energy features obtained using 2-D discrete wavelet transform. The energy features obtained from the detailed coefficients can be used to distinguish between normal and glaucomatous images with very high accuracy. The effectiveness is evaluated using K-NN classifier by taking 30 normal and glaucoma images, 15 images are used for training and 15 images for testing.
INTRODUCTION
Glaucoma is the second leading cause of blindness worldwide. Glaucoma is caused due to the increase in intraocular pressure of the eye. The intraocular pressure increases due to malfunction or malformation of the drainage system of the eye. The anterior chamber of the eye is the small space in the front portion of the eye. A clear liquid flow in and out of the chamber and this fluid is called aqueous humor. The fluid, aqueous humor nourishes and bathes nearby tissues. The intraocular pressure of the eye is maintained by the aqueous humor. The pressure within the eye is maintained by producing a small amount of aqueous humor while an equal amount flows out of the eye through a microscopic drainage system called trabecular meshwork. Glaucoma is mainly caused due to increase in intraocular pressure. Increased intraocular pressure results from either increased production or decreased drainage of aqueous humor. The increased intraocular pressure within the eye damages the optic nerve through which retina sends light to the brain where they are recognized as images and makes vision possible. Hence elevated intraocular pressure is considered a major risk factor for Glaucoma. The prevalence of Glaucoma in worldwide is increasing rapidly .This is due in part to the rapidly aging population. Blindness due to Glaucoma greatly impacts the independence of many people who are part of this aging population. A prevalent model estimates that at the current time, there are about 60 million people worldwide with Glaucoma. Thus Glaucoma has become the second leading cause of blindness worldwide. Thus it becomes necessary to detect Glaucoma earlier and can provide better treatment. In this paper, we proposed a novel method to detect Glaucoma at an early stage by differentiating Glaucoma affected retinal images from normal retinal images by extracting the energy signatures from the provided dataset using two dimensional discrete wavelet transform and subject them to classification process. In this paper, we propose the use of 3 different wavelet filters such as daubechies, symlets and reverse biorthogonal on a set of fundus images by employing 2-DDWT. The texture features using wavelet transforms in image processing are often employed to overcome the generalization of features. We calculate the averages of the detailed horizontal and vertical coefficients and wavelet energy signatures obtained by wavelet decomposition. The extracted features are subjected to feature selection procedure to determine the combination of relevant features to maximize the class similarity.
CONCLUSION
This paper demonstrates the feature extraction process using three wavelet filters. The daubechies, symlets and reverse biorthogonal are the wavelet filters used. The wavelet coefficients obtained are then subjected to average and energy calculation resulting in feature extraction. The classification is done using K-Nearest Neighbour classifier which provides higher accuracy. We can conclude that the energy obtained from the detailed coefficients can be used to distinguish between normal and glaucomatous images with very high accuracy.