Sumeet Dua and Mohit Jain

also provide the clinician with precise information about the eye disease, thus aiding in quick treatment and reducing the risks of visual disability.

3.4.1.Computational Decision Support System: Diabetic Retinopathy

Diabetic retinopathy occurs when neurons stop transmitting signals from the retina to the brain, damaging the blood cells in the retina, and can lead to severe vision loss or blindness, and a diabetic retinopathy patient may not notice changes to his or her vision at the beginning of the disease. Late diagnoses may cause lasting problems, because, if this disease is not cured early, treatment may be unsuccessful. If the disease is detected early, the worst-case can be anticipated, and the patient and ophthalmologist can prepare appropriately. A computerized interactive system can screen a large number of retinal images and classify abnormal and normal images, saving the physician time, which can then be spent on surgery and treatment. Computer decision support system methodologies for ophthalmology are wavelet-based neural network and content-based image retrieval (CBIR) based on wavelet transform coefficients distribution.21,22 Steps to implement the wavelet-based network are shown in Fig. 3.7.

3.4.1.1. Wavelet-based neural network23

Applying wavelet-based neural networks to diagnose diabetic retinopathy requires four steps, as described below.

First, color retinal images can be acquired using Nikon, Sony, or fundus cameras and can be used by an ophthalmologist for diagnosing a patient. Once these images are acquired, they are preprocessed using the steps shown in Fig. 3.8. The RGB color system is converted to HIS as an optic disc and is one of the brightest regions in the image. Then, the disc is converted to a gray-scale image. Next, an image histogram is applied to adjust the brightness or intensity level of the image. Last, noise is removed. To reduce noise from the image, a median filter or Gaussian filter is applied.

Second, the discrete wavelet transform is used to extract features, such as an optic disc, which acts as the reference for other features.23 Blood vessels help ophthalmologists to analyze diabetic retinopathy over the course of the disease. These blood vessels can be extracted through segmentation with

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Image

Acquisition

Image

Preprocessing

Wavelet-Based Feature

Extraction

Feature

Selection

Classification Using Neural

Networks

Fig. 3.7. Computational steps for wavelet-based neural network.

wavelet transform. Exudates are lesions that commonly occur in diabetic retinopathy. The shape and brightness of exudates vary from patient to patient and can be detected using a thresholding approach. The identification of the changes in blood vessels and exudates in the retina over time can aid physicians in making early detections of diabetic retinopathy.

Third, a feature selection method, such as the ChiSquared attribute, the GainRatio attribute, and the infogain attribute, is used to select the important or relevant features of an image. By selecting important features, a clinician can speed up the learning process or remove the effect of the curse of dimensionality.

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RGB Image Is Converted to HIS

HIS Is Converted to Gray-Scale Scale

Image Histogram Is Appplied

Noise Is Removed

Fig. 3.8. Computational steps for image preprocessing.

Fourth, the features that are extracted during feature selection become input for the neural network. In this step, a classifier helps the clinician to classify images into healthy or diseased eyes categories. First, to construct a model using input, the features selected to build or train the model can distinguish images of different classes. This automated system can be used to analyze the retinal images of different classes using wavelet-based extraction, and an image classifier based on artificial neural network can classify the images according to disease conditions.

3.4.1.2. Content-based image retrieval

CBIR is the application of computer vision in which images are retrieved according to the image content, for example, color, shape, textures, or any other information from the large database. The steps to retrieve images using CBIR are shown in Fig. 3.9.

CBIR22 is based on retrieving the K nearest neighbor (KNN) using similarity measures based on the input image means to find all the nearest matches of the input image and its procedure or steps are drawn in Fig. 3.10.

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Fig. 3.9. Computational steps for CBIR.

Fig. 3.10. Steps for retrieving images using similarity measures.

To explain this methodology, let us say that a clinician has 500 retinal images in his or her database and one input or query image. First, the clinician should extract the features of all the images using feature-extracted algorithms (explained in the Sec. 3.3.1). Once the clinician extracts the

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