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Application for Recognition of Human Eye Retina
Drozd, Radek ; Hájek, Josef (referee) ; Drahanský, Martin (advisor)
The blood vessels layout in a human eye retina is unique for every person in the world, so it is one of important biometric characteristics. Processing of colour retina image may be a part of an intended biometric system. There is an algorithm for automatic blood vessels detection, optic disc and macula localisation, finding of bifurcation points and saving those as a biometric template presented in this bachelor's thesis. C++ programming language and OpenCV library were used for implementation. The application was tested on a set of colour retina images, taken by fundus camera. The final application is supposed to run on a digital signal processor, developed by Texas Instruments. The thesis gives the introduction into biometrics, signal processing and human eye anatomy.
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Segmentation in the color fundus imges
Malínský, Miloš ; Jiřík, Radovan (referee) ; Kolář, Radim (advisor)
Optic nerve head and macula are important structures in fundus images. Detection and measurement plays crucial role in several diagnosis methods of optic disease. This work is focused on the detection of the central point of macula and optic nerve head, where the inner border is detected too. There are many methods for extracting this structure in retinal images. Due to the unique properties of each acquisition technique, a single generally acknowledged detection algorithm does not exist. The whole detection process is described from preprocessing through segmentation towards postprocessing. Presented methods are based on the combination of correlation techniques, Hough transform, active contours and morphological operations. The detected contours of the optic nerve head are evaluated and quantitatively compared with the contour drawn by experienced ophthalmologist. The master thesis contains quantity of images that help to describe detection methods.
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Automated Fovea Center Estimation In Adaptive Optics Images
Valterova, Eva
The adaptive optics (AO) is an insightful tool with high potential. Since the first demonstration in ophthalmology has undergone immense growth in its diagnostic application, mainly because the AO enables to capture retina in vivo with resolution on the cellular level. In the center of the retina is macula, containing the fovea. The fovea detection is possible with utilizing the knowledge of its characteristical properties. We developed and applied two methods for fovea center estimation. The first method is based on image maximum detection. The second is based on the 2D Gaussian curve fitting. The first method has proved better results in comparison with manual grading. The measured difference was 32_x0006_27 pixels. The second approach has in comparison with manual grading slightly higher difference equal to 108_x0006_64 pixels. Both of the methods estimated the foveal center within the expected foveal area.
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Application for Recognition of Human Eye Retina
Drozd, Radek ; Hájek, Josef (referee) ; Drahanský, Martin (advisor)
The blood vessels layout in a human eye retina is unique for every person in the world, so it is one of important biometric characteristics. Processing of colour retina image may be a part of an intended biometric system. There is an algorithm for automatic blood vessels detection, optic disc and macula localisation, finding of bifurcation points and saving those as a biometric template presented in this bachelor's thesis. C++ programming language and OpenCV library were used for implementation. The application was tested on a set of colour retina images, taken by fundus camera. The final application is supposed to run on a digital signal processor, developed by Texas Instruments. The thesis gives the introduction into biometrics, signal processing and human eye anatomy.
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Segmentation in the color fundus imges
Malínský, Miloš ; Jiřík, Radovan (referee) ; Kolář, Radim (advisor)
Optic nerve head and macula are important structures in fundus images. Detection and measurement plays crucial role in several diagnosis methods of optic disease. This work is focused on the detection of the central point of macula and optic nerve head, where the inner border is detected too. There are many methods for extracting this structure in retinal images. Due to the unique properties of each acquisition technique, a single generally acknowledged detection algorithm does not exist. The whole detection process is described from preprocessing through segmentation towards postprocessing. Presented methods are based on the combination of correlation techniques, Hough transform, active contours and morphological operations. The detected contours of the optic nerve head are evaluated and quantitatively compared with the contour drawn by experienced ophthalmologist. The master thesis contains quantity of images that help to describe detection methods.
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