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histograms, each of which correspond to a distinct section of the image, and uses them to redistribute the pixel values of the image.

10.3. Feature Extraction

Features, namely (i) blood vessels, (ii) exudates, and (iii) hemorrhages, were extracted from the fundus images. A brief description of these features is given below.

10.3.1. Blood Vessel Detection

The number of blood vessels, which nourish the retina, is one of the features that define the different DR stages. In this work, we used various morphological image-processing methods to identify the blood vessels. Figure 10.4 is a flowchart illustrating the blood vessel detection algorithm. In our experiments, the green channel of the fundus RGB image was used to detect blood vessels.14 The image was converted to grayscale and subjected to an image inversion operation. The adaptive histogram operation was used to

Fig. 10.4. Block diagram of the blood vessel detection.

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improve the image contrast. The morphological “opening” operation was implemented, using the “ball” structuring element, to highlight the blood vessels and smoothen the image background.13 After that, the inverted image was subtracted from the enhanced image. In the resulting image, the blood vessels showed higher contrast than the background.

The resulting image was binarized with a threshold value of 0.099, and, then, median filtering was performed to remove noise. To extract blood vessels from fundus images, a border was created and all remaining noise in the image was eliminated using the MatLab function “imfill”. The images that showed only borders were subtracted from the inverted image, effectively removing the borders. Next, the image was inverted again to obtain an image depicting only blood vessels.

10.3.2. Exudates Detection

The green component of the RGB image was used for exudates detection.14–16 Figure 10.5 shows the flowchart for this process. Dilation and erosion techniques using octagon and disc-shaped structuring elements were used to detect the exudates. The “Closing” operation was implemented using an octagon-shaped structuring element to obtain a better contrast image.13 The resultant image contained both optic disc and exudates. Furthermore, the image gray levels are comparable with the exudates.

In order to remove artifacts from the image, neighborhood operation was performed by columns. The result of this operation was an image, which contained only optic disc and exudates. It is common for exudates to have irregular shapes. A disc-shaped structuring element was used to solve this problem (irregular exudates) and, then, the resultant image was binarized with a threshold value of 0.7 to highlight only the exudates. In most fundus images, the optic disc had the highest pixel value and occupied approximately one-seventh of image. A mask of 80 × 80 was used at the highest pixel location to remove the optic disc. Finally, the opening operation was performed using a disc-shaped structuring element to obtain only exudates.

10.3.3. Hemorrhages Detection

Figure 10.6 shows the two steps involved in hemorrhage detection: (1) detection of blood vessels alone and (2) detection of both blood vessels and

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Fig. 10.5. Block diagram of the exudates detection.

hemorrhages. Once blood vessels and hemorrhages were detected, the image with blood vessels alone was subtracted from the image with blood vessels and hemorrhages to obtain only the hemorrhages. The red channel of the RGB image was used to detect hemorrhages.14 An inversion operation was performed on the RED channel of the image. Next, the image was subjected to adaptive histogram equalization to increase the image contrast. Two “ball” shaped structuring elements, of sizes 6 and 25, were used to detect blood vessels without hemorrhages and blood vessels with hemorrhages.14 We have divided the detection of blood vessels without hemorrhages and the detection of blood vessels with hemorrhages into two steps, as given below.

For the detection of blood vessels without hemorrhages, a small “ball”- shaped structuring element of size 10 was used, and the image was subjected to both erosion and dilation. Next, the image was enhanced using adaptive histogram equalization. Finally, the resulting image was subjected to image enhancement again to highlight only the blood vessels.

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Fig. 10.6. Block diagram of the hemorrhages detection.

To detect both blood vessels and hemorrhages, we used a “ball”-shaped structuring element of size 45, because hemorrhages are typically large. First, the image was subjected to erosion and dilation using this “ball”- shaped structure element. Then, the image was enhanced to increase the image contrast. Next, this image was dilated and, then subtracted from the enhanced image and subjected to histogram equalization, again to obtain the image with blood vessels and hemorrhages.

The image that did not contain hemorrhages was subtracted from the image with blood vessels and hemorrhages to obtain an image with hemorrhages and noise. The resulting image was binarized with a threshold value

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