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A. Budai, J. Hornegger, G. Michelson, R. P. Tornow; Optical Density Measurement of Macular Pigment. Invest. Ophthalmol. Vis. Sci. 2010;51(13):4410.
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© ARVO (1962-2015); The Authors (2016-present)
To improve computer aided optical density measurements of the macular pigment by semi-automatic analysis of the macula region of retinal color fundus images.
Retinal fundus images are acquired with a common fundus camera (FF 450 Carl Zeiss Meditec) with a special 3-band transmission filter in the illuminating beam path of the fundus camera. The digital camera was set to a linear transfer function for all three channels. The images show no under- or oversaturation in any of the color channels. After a ROI selection the algorithm calculates the presented features without any user interaction. A registration method is applied on the green and blue channels to correct the wavelength dependent aberration of the light. The vessels of the images are excluded from the whole examination process using a vessel segmentation algorithm. An assessment of optical pigment density for each pixel in the macula region is calculated from the ratio of the blue and green channel values. This value is normalized by a multiplier calculated from the ratio of green and blue values at a circle of 8 degree from the foveal center. Using the calculated densities different properties of the density peak are calculated like the center point, standard deviation and density profile, which shows the optical pigment density as a function of the distance from the center. An example of calculated density image and the corresponding density profile can be seen in the figure.
The algorithm was tested on 30 images of 3 subjects. The robustness was tested comparing measurements based on 9 images of the same patient. These images were taken using significantly changing illumination intensities from under-illuminated close to over-saturated. The calculated density profiles showed similar curves, with 15% intensity variation in average. This variance is caused mostly by the illumination differences.
The presented algorithm provides a robust optical pigment density profile of macular pigment density. Only one user interaction is required during the whole process.
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