Initial Background Selection by Otsu Method. We employed the automatic histogram–based thresholding technique known as the Otsu method25 in each zone to provide initial input to the background model. Briefly, let the pixels in the green channel be represented in L gray levels [1, 2, . . . , L]. Suppose we dichotomized the pixels into 2 classes, C0 and C1, by a threshold at level k. C0 denotes pixels with levels [1, . . . , k] and C1 denotes pixels with levels [k + 1, . . . , L]. Ideally, C0 and C1 would represent background and drusen. A discriminant criterion that measures class separability was used to evaluate the goodness of the threshold (at level k). The Otsu method uses the criterion of between-class variance and selects the threshold k that maximizes this variance.25 The Otsu method can be generalized to the case of 2 thresholds k and m, where there are 3 classes, C0, C1, and C2, defined by pixels with levels [1, . . . , k], [k + 1, . . . , m], and [m + 1, . . . , L], respectively. In a given image, these classes might represent background, objects of interest, and other objects (eg, retinal vessels), in some permutation. The criterion for class separability is the total between-class variance σB2 = ω0(μ0 − μT)2 + ω1(μ1 − μT)2 + ω2(μ2 − μT)2 where ωi and μi are the zero-order and the first-order normalized cumulative moments of the histogram for class Ci as defined above for i = 1, 2, 3, and μT is the image mean. The solution is found by the finite search on k for k = 1, . . . , L − 1 and m for m = k + 1 to L for the maximum of σB. The Otsu method may also be performed sequentially to subdivide a given class. That is, if a given class, C, is already defined (by Otsu or otherwise), then C may be treated as the initial histogram (setting other histogram values to zero), and one can apply an Otsu method to subdivide C into 2 (or 3) classes.