2. Proposed retinal identification algorithm

Fig. 2 shows block diagram of proposed retinal identification algorithm. At first, to extract features from the retinal images, the blood vessels are segmented. In the segmented vessels of retinal images, to overcome the issues caused by eye-movement, the surrounded regions by the blood vessels are considered as Regions Of Interest (ROI). These regions are formed and optimized from the segmented vessels using watershed algorithm. In the next step, geometrical shape features based on boundary and region are extracted from the defined ROIs. In the matching step, a fast modified hierarchical matching structure in interact with both the feature extraction and decision making steps is applied whose basic task is to match the query ROI and all enrolled ROIs in database. The identification procedure is carried out in a decision making scenario according to the matched ROIs. In the following, the details of proposed identification algorithm are described.

Fig. 2: Block diagram of the proposed retinal identification algorithm.

2.1. Vessel Extraction

To extract the blood vessels, we employ a fast efficient algorithm introduced in our previous work [29] which could partly eliminate some destructive factors such as different illumination conditions and improved contrast of the retinal images. Then, a fast accurate algorithm represented by Saleh et al. [30] is performed to obtain a binary image consisting of the blood vessels.

2.1.1. Preprocessing

As previously mentioned, different illumination conditions beside physical traits of retina cause non-uniform reflection during capturing process. Moreover, poor contrast of the captured images induces a preprocessing algorithm especially where the vessel segmentation is required. Here, we perform an efficient preprocessing algorithm presented in [29] which uses the red and green sub-bands of retinal images to obtain a desirable image as input for the segmentation.

In imaging, an image can be modeled by the illumination and reflection components as,

(1)

where and are terms of illumination and reflection, respectively. In the retinal images, the green band is usually used due to better contrast while the red channel doesn’t give any proper information about the vessels and the blue channel is too noisy. But, the red channel hands valuable information about the illumination pattern during the capturing process. Accordingly the averaged red band and green channel are considered as the illumination and input image (i.e. ), respectively. At last, the reflection component is considered as enhanced image that gives a more desirable image in which the blood vessels are more recognizable and also the contrast of image particularly around OD, fovea and marginal regions is improved. The enhanced image is obtained by Eq. (1) as,

(2)

where and are the green and averaged red channels of retinal image, respectively.