Sumeet Dua and Mohit Jain

features, then he or she computes the distance between the input image and the database images one by one, using similarity measures, such as Euclidean distance and the dynamic time warping algorithm. In this way, the clinician gets 500 values, and out of those 500 values, the clinician retrieves KNNs or the nearest matches of those query images having a minimum distance between them. Using the KNN method of CBIR, the clinician can better distinguish between the healthy and diseased eyes by retrieving the nearest matches of query image.

3.4.2. Computational Decision Support System: Cataracts

Cataract is an eye disease that is developed in the crystalline lens of the eye and is the leading cause of blindness worldwide.24 Cataracts develop slowly, and the patient is normally not aware of the gradual loss of eyesight. If untreated, this disease can result in vision loss in both eyes. An early diagnosis of the disease has a greater chance of preventing blindness and curing the disease. A cataract can be age-related (further classified as a nuclear or cortical cataract), congenital, or trauma-related.25 The causes of cataracts include exposure to the sun and high radiation, diseases such as diabetes and hypertension, trauma, aging, or genetics.

Surgery is the most effective and common way to treat cataracts; phacoemulsification is the most popular form of cataract surgery. During phacoemulsification, surgeons classify the eye into the normal or diseased eye categories. Due to the advancement of image processing and machinelearning techniques, computer aided classification can be used to diagnose and classify the images and to aid decision support.

As with diagnosing diabetic retinopathy, to diagnose a cataract, the computer decision support system must perform image acquisition, image preprocessing (equalization and binarizing), feature extraction, feature selection, and image classification.4,26,27 These steps are described below.

First, in image acquisition, lit-lamp cameras are used to capture the details of the eye structure by changing the parameters or width of the beam. Then, images are divided into training and testing images. Training images are used to build the model and to aid in the classification of the images.

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Computational Decision Support Systems and Diagnostic Tools

Second, image preprocessing for computer decision support is a threestep process. The images are equalized, binarized, and converted to gray-scale. Equalization is performed to make the intensity, brightness, or contrast of all images the same. Equalization can be performed by using image histograms to detect the images of healthy and diseased eyes. Binarization is performed with a carefully chosen threshold. The RGB image is converted into gray-scale, and it carries only intensity information. Grayscale images vary from black at the weakest intensity to white at the strongest and are easier to handle than RGB images.

Third, in feature extraction, features, the key or unique points of an image, are extracted from the raw images, which are based on the lens structure. Features help to extract the important information from the image, which helps to distinguish between the healthy and diseased eyes. Examples of features are a big ring area, a small ring area, homogeneity, and BW morph. A big ring area can result when the color at the outer surface of the cornea is not the same in all classes; some colors are brighter and some are dimmer. A small ring area can result when the color brightness at the inner surface of the cornea is not same in all the classes. Homogeneity measures the closeness of the distribution of the elements. BW morph is a morphical operation performed on the binary images.

Normal cataract images have too many sudden changes in the gray levels. After binarization, the image is converted to black and white to find the area between the images. Other features, such as mean intensity inside the lens, color on the posterior reflex, mean intensity of the sulcus, and the intensity ratio between the anterior and posterior lentils, can be extracted using principal component analysis and regional features using a gray level cooccurrence matrix such as energy, homogeneity, correlation, or contrast.

Fourth, feature selection is also known as attribute selection or feature reduction. During feature selection, the computational system will select the important or relevant features. A clinician can perform a feature selection method such the ChiSquared attribute, the GainRatio attribute, or the infogain attribute.

Fifth, once features have been selected, the clinician can classify the images in two ways. He or she can use a classifier, such as a support vector machine, a neural network, or a fuzzy classifier, or can use KNNs.

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