*4.5.4 Visualization program*

*Applications of Pattern Recognition*

several images of each eye.

**4.5 Developed applications**

two entered retina patterns.

*4.5.1 Manual marking program*

*4.5.2 Automatic marking program*

*4.5.3 Compare program*

mology clinics generated the gold standard data.

are from same person eye (further as "school database").

images using Messidor's e-optha and HRF databases.

age of 48 features was found in these retinal images.

Next is High-Resolution Fundus image database from German Fiedrich-Alexander University. The *HRF* database has 15 images of healthy patients, 15 images of patients with diabetic retinopathy, and 15 images of glaucomatous patients. Each image has a binary gold standard vessel segmentation image. Moreover, particular datasets are provided with masks to determine the field of view (FOV). A group of experts from the retinal image analysis field and the medical staff from the cooperating ophthal-

The EBD is internal set of iris and retina images our research group STRaDe (Security Technology Research and Development at the Faculty of Information Technology, Brno University of Technology (CZ), focused on security in IT and biometric systems). The database contains 684 images of both retinas from 110 distinct people, totaling 220 distinct samples. Unfortunately, a significant part of this set consists of very low-quality pictures. But in this database all persons have

For additional checking of our algorithms we use our retinal fundus camera at our laboratory, which we make for several past years. We use 30 images from students, which captured during Biometric systems course. Some images have bad quality, which is useful for testing applications in a worse condition. Several images

We developed several application software modules to determine some properties of the retina, which will then be used to find out the degree of similarity of the

The first program (SW1) is developed for manual retina marking by our students. First, the edges of the optical disk are marked. The program stores the top left position and the width and height of the ellipse around the optical disk. Then, the fovea is marked. Both positions are stored in Cartesian coordinates, which are based on the image properties and resolutions. Each feature is then marked after both main structures in the retina. These points are stored in polar coordinates. Data from the images are stored as a plain text file. By this program, we marked all retinal

The second program (SW2) stores the same information about the image as SW1, except that it performs the steps automatically. Details of the overall work of the program, its steps, and further development are summarized in work [5]. The program is developed in Python and was used on the same images as SW1. An aver-

SW3 compares the detection accuracy between the manually marked-up results by SW1 and the automatically marked-up results by SW2. The algorithm is designed to compare the bifurcations/crossings that were selected manually, with the automatically detected set after they have been detected. The paired bifurcations/cross-

ings are automatically found through a method like that in chapter 4.3.

**88**

SW4 collects the marked data by SW1 and SW2 in the previously described text file format into one picture. It collects individual pixels into a grid of adjustable size. For our purposes, a summary grid of 5 × 5 pixels was chosen as the most suitable. In the images, the fields with a higher frequency of occurrences are colored darker.
