Rod rhodopsin measurement (I) and Blood vessel detection.

<div><p>We determined <i>b</i> ≈ 0.04 through the largest count in the histogram, which corresponds to the fit associated to an average of a large annulus area. The gradient descent minimization scheme led to the optimized parameters </p><p></p><p></p><p></p><p><mi>a</mi><mo>^</mo></p><p></p><p></p><p></p> and <p></p><p></p><p></p><p><mi>c</mi><mo>^</mo></p><p></p><p></p><p></p>, where the macular pigment distribution was used to refine the initial value <i>a</i>₀. Blood vessels show up as artifacts in the resulting rhodopsin maps and must still be removed.<p></p> <p>We detect blood vessels through a scheme that evolves from the computational minimization of <i>E</i> related to the rhodopsin model. Spatial regions where the numerical algorithm converges significantly slower than in other image parts shall be identified as blood vessels. The number of required iterations leads to an image which can simply be thresholded to identify retinal blood vessels.</p> <p>(a) spatial map of parameter </p><p></p><p></p><p></p><p><mi>c</mi><mo>^</mo></p><p></p><p></p><p></p> optimal within the model. Brighter means increase in rhodopsin density, but brightest spots occur as registration artifacts along blood vessels. Therefore, the grayscale colormap is suppressed.<p></p> <p>(b) extracted mask of blood vessels derived from thresholding the number of iterations needed for convergence in the optimization scheme. Simple pixel growth in the mask would lead to complete coverage of the vessels.</p>