Fundus Image Classification Using Wavelet Based Features in Detection of Glaucoma

Published by Oriental Scientific Publishing Company © 2018 This is an Open Access article licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/ ), which permits unrestricted Non Commercial use, distribution and reproduction in any medium, provided the original work is properly cited. Fundus Image Classification Using Wavelet Based Features in Detection of Glaucoma

Glaucoma makes harm the eye's optic nerve and deteriorates after a period of time as shown in Figure .1. It's frequently connected with a development of pressure inside the eye.The expanded pressure, called intraocular pressure can harm the optic nerve which passes on the pictures to our brain.On the off chance that harms to the optic nerve from high eye pressure proceeds with, glaucoma will cause perpetual loss of vision.
There is a little space in front of eye named as the "frontal chamber".Clear fluid streams all through the foremost chamber which feeds and showers adjacent tissues.On the off chance that a patient has glaucoma, the liquid does not deplete appropriately -it empties too gradually -from the eye.This prompts liquid develop, and weight inside the eye rises.Unless this weight is cut down and controlled, the optic nerve and different parts of the eye may wind up harmed, prompting loss of vision.Without treatment, glaucoma can cause add up to perpetual visual deficiency within a couple of years.Since the vast majority with glaucoma have no early indications or agony from this expanded pressure, it is critical to see eye specialist frequently so glaucoma can be analyzed and treated after a short time term visual misfortune happens.Glaucoma has a chance to occur over 40 age and family history of glaucoma.It is necessary to have a complete eye exam once in every two year.If left untreated the patient may loss vision, and even become blind.The disease generally affects both eyes, although one may have more severe signs and symptoms than the other.

Literature survey
The most important step in the development process of the automation system is the documentation of comprehensive review of the published and unpublished work from secondary data sources in the areas of specific interest to the researcher.A review more often than not goes before the proposed framework and results segment.Its definitive objective is to convey the reader in the know regarding outgoing flow and structures the reason for another objective.A very much organized survey is described by an intelligent stream of thoughts, momentum and applicable references with predictable, suitable referencing style, appropriate utilization of wording, and a fair and extensive perspective of the past research on the theme.Ganesh Babu et al present a new method for detection of glaucoma in the retinal images.The proposed system has three stages such as ROI Extraction, Feature Extraction and Classification.Digital image processing methods, for example, preprocessing, morphological tasks and thresholding, are generally utilized for the programmed recognition of optic disc, veins and estimation of the features.The features extracted are Cup to Disc Ratio (CDR) and the proportion of vessel area.K-means clustering method is utilized to compute the CDR feature and are validated by classifying the normal and glaucoma images using neural network classifier.
Acharya et al introduced glaucoma detection utilizing a mix of texture and higher order spectra (HOS) features.The results show that the texture and HOS includes after z-score standardization and feature determination, and when joined with a random forest classifier, performs superior to anything alternate classifiers and accurately distinguishes the glaucoma images with a decent exactness.The effect of feature positioning and standardization is additionally Huang.K et al done vitality circulation over wavelet sub groups is a generally utilized element for wavelet packet based texture arrangement.Because of the over total nature of the wavelet packet disintegration; include determination is typically connected for better grouping precision and minimal element portrayal.The larger part of wavelet feature choice calculations lead include choice in light of the assessment of each sub band independently, which verifiably accept that the wavelet features from various sub groups are autonomous.The reliance between features from various sub groups is researched hypothetically and mimicked for a given picture demonstrates.In view of analysis and simulation, a wavelet feature determination calculation in view of factual reliance is proposed.This algorithm is additionally enhanced by joining the reliance between wavelet include and the assessment of individual wavelet feature part.The results show the viability of the algorithms in fusing reliance into wavelet feature determination.
A.Arivazhagan et al defines the use of wavelet transform for classifying the texture of images by describing a new method of feature extraction for description and segmentation of texture at numerous scales based on block by block assessment of wavelet co-occurrence features.The steps involved in feature segmentation are The execution of this algorithm is better than conventional single determination strategies, for example, texture spectrum, co-events and so on.
The aftereffects of the proposed algorithm are observed to be acceptable.Efficiency is improved by removing noise using disk ûltering and thresholding techniques.
Paul Y. Kim et al explores the utilization of fractal examination (FA) as the premise of a framework for multiclass forecast of the movement of glaucoma.FA is connected to pseudo 2-D pictures changed over from 1-D retinal nerve fiber layer information acquired from the eyes of ordinary subjects, and from subjects with dynamic and non dynamic glaucoma.FA features are acquired utilizing a box-counting technique and a multifractional Brownian movement strategy   Texture assumes a critical part in numerous machine vision errands, for example, surface examination, scene classiûcation, surface introduction and shape assurance.Texture is portrayed by the spatial dissemination of gray levels in an area.Analysis of texture requires the identiûcation of proper attributes or features that differentiate the textures in the image for segmentation, classiûcation and recognition.The features are assumed to be uniform within the regions containing the same textures.Textural

Proposed system
The objective of this work is to differentiate normal eye images and glaucoma affected eye images.This classification is based on selecting eminent features using wavelet filters.The wavelet features are used to train the supervised classifier which classifies retinal images into normal or abnormal and achieve a high a level of accuracy as shown in Figure .3.It is proposed to use a wavelet filter is daubechies.This filter is used to calculate energy and average value for the image.With the help of this filter, the wavelet coefficients are obtained and their characteristics like average, energy, standard deviation and variance are resulting in feature extraction.This filter will increase the accuracy and performance of an image.This filter is examined by employing a standard 2-D discrete wavelet transform (DWT) which are used to extract features and analyze changes.The extracted features are fed into supervised classifier such as Neural Network.
The figure 6.1 describes about the overall process architecture of our project.There will be sets like training set and testing set.In training set the input images that are to be trained are preprocessed.The preprocessing is carried out like scaling and selecting green channel.Then from the preprocessed input images, features like average, energy, standard deviation and variance are extracted.Then these extracted features are fed into Neural Network classifier for classification process.In testing set, each image that is tested whether it is normal or abnormal

Preprocessing
The objective of preprocessing is to improve the interoperability of the information present in images for human viewers.An enhancement algorithm is one that yields a better quality image for the purpose of some particular application which can be done by either suppressing the noise or increasing the image contrast.Image preprocessing includes emphasis, sharpen or smoothen image features for display and analysis.Enhancement methods are application specific and are often developed empirically.Image preprocessing techniques emphasis specific image features to improve the visual perception of an image.

Image decompositon using wavelet transforms
The DWT confine the spatial and frequency information's of an indication.DWT breaks down the image by disintegrating it into a coarse estimate through low-pass filtering and into detail data by means of high-pass filtering.Such decay is performed recursively on low-pass estimate coefficients got at each level, until the point when the vital emphases are come to.Each image is represented to as a p × q gray scale network I [i,j], where every component of the framework speaks to the grayscale intensity of one pixel of the image.Each nonborder pixel has eight nearby neighboring pixel intensities.These eight neighbors can be utilized to navigate the network.The resultant 2-D DWT coefficients are the same regardless of whether the lattice is crossed ideal leftto-right or right-to-left.Henceforth, it is adequate that we consider four directions comparing to 0ae% (Dh), 45ae% (Dd), 90ae% (Dv), and 135ae% (Dd) orientations.The decay structure for one level is represented in Figure .4.In this work daubechies (db4) wavelet filter is used.After decomposition, it reproduces the original image without loss of any information.The proposed system contains features of average or mean, energy, standard deviation and variance.

Feature extraction
It is used to select important features of an image.Feature extraction is a particular form of dimensionality reduction.If the input data is too large to be processed, then the input information will be transformed into a condensed set of features (also named features vector).The features extracted are carefully chosen such a way that they will extract the relevant information to carry out the desired task.This approach is successful when images are large and an optimized features is required to complete tasks quickly.
Investigation with an extensive number

Standard deviation
The standard deviation (SD) as stated in Equation ( 7) describes the variation from the average exists.A low SD point towards that the data points are close to the mean.A high SD point towards that the data points are extensive over huge values.

...(7) Variance
The difference measures how far an arrangement of numbers is spread out.(A difference of zero demonstrates that every one of the qualities are indistinguishable.)Variance is dependably nonnegative.A little fluctuation demonstrates that the information directs incline toward be near the mean (expected esteem) and henceforth to each other, while a high change shows that the information calls attention to extremely spread out from the mean and from each other.The square foundation of change is known as the standard deviation.Square of standard deviation gives an estimate spread of pixel value around the image mean.

Image classification
Classification of data is used to assign corresponding levels with the aim of discriminating multiple objects from each other within the image.The level is called as class.Classification will be executed on the base of spectral or spectrally defined features, such as density, texture etc.It can be said that classification divides the feature space into several classes based on a decision rule.In this work, the classification process has been carried out as per the flow diagram shown in Figure .5.
Neural systems are made out of basic components working in parallel.These components are motivated by natural sensory systems.As in nature, the associations between components generally decide the system work.Neural system can be prepared to play out a specific capacity by changing the estimations of the associations (weights) between components.Commonly, neural systems are balanced, or prepared, with the goal that a specific info prompts a particular target yield.The system is balanced, in light of a correlation of the yield and the objective, until the point when the system yield coordinates the objective.Regularly, numerous such info/target sets are expected to train the system.

RESULTS AND DISCUSSION
The feature information extracted for healthy and glaucomatous input image along horizontal, vertical and diagonal axis is tabulated in Table 1 to Table 6.The values are calculated for average for red, green and blue channels.Energy, standard deviation and variance are calculated for green channel.
The features along horizontal, vertical and diagonal axis of the image extracted are fetched as input to FFBNN classifier in order to train and test the system.The neural network performance platform and the classification output for both healthy and abnormal image is shown in Figure .6 to Figure.9.
Table 7 gives the results of the proposed work on HRF dataset shows the performance evaluation of classification of fundus image, and an average sensitivity rate of 100% and specificity rate of 91.67% and accuracy rate of 95.83% on HRF dataset are obtained.
Table 8 shows the performance comparisons of the proposed classification methodology with the authors Kiran S.M et al (2016) and N. Annu et al (2013).The proposed methodology achieves 95.83% average accuracy compared with the Kiran S.M et al (2016) achieves 91.66% of an average accuracy and N.Annu et al (2013) achieves 95% of an average accuracy.This proposed classification methodology on HRF dataset provides better sensitivity and higher accuracy.

CONCLUSION
The proposed method to classify the fundus image into normal and glaucomatous image is carried out in this work.The results obtained yield an accuracy rate of 95.83%.The study also illustrates the efficacy of waveletbased feature techniques for detecting and predicting glaucomatous progression.From the accuracies obtained, it can be concluded that the average, energy, standard deviation and variance are obtained from the detailed coefficients that can be used to distinguish between normal and glaucomatous images with very high level accuracy.

Fig. 6 .Fig. 7 .Fig. 8 .
Fig. 6.GUI showing performance validation using NN classifier for normal image of factors by and large requires a lot of memory and computation control or classification method which over fits the training sample and sums up inadequately to new examples.Best outcomes are accomplished when a specialist develops an arrangement of use of application dependent features.Average or mean Average feature helps to measure the center tendency of data.Average is Add up all the numbers (pixels) and then divided by the number of pixels.It is calculated from the decomposition image in directions of horizontal, vertical and diagonal as stated in Equation (1 to 3)....(1) ...(2) ...(3) Energy It is the proportion of each channel takes up in original color image or which channel (Red, Green and Blue) has the bigger proportion and has been calculated as stated in Equation (4 to 6)

Table 2 .
Healthy image-horizontal detail

Table 3 .
Healthy image vertical detail

Table 4 .
Glaucoma image vertical detail

Table 5 .
Healthy image diagonal detail

Table 6 .
Glaucoma image diagonal detail

Table 7 .
Performance measurement

Table 8 .
Performance comparison of Glaucoma classification