## 4 Discussion  
In the present study, we compared computer-assisted cone counts on high-pass filtered images to the average counts of two independent human graders and consequently identified optimal high-pass filter settings in CACD patients and healthy subjects. Photoreceptor cells were clearly visible in every high magnification module image acquired in our study. The visibility of photoreceptors was sufficient to grade the corresponding images as \"good quality\", according to the high magnification module grading system proposed by [PERSON] et al. [11]. Nonetheless, after utilizing a Gaussian sigma radius of 1.5 \(\upmu\)M, the analysis of 6 ROIs of healthy participants resulted in automated cone counts outside of the limits of agreement on the corresponding Bland-Altman plot. In CACD patients analyzed via a Gaussian sigma radius of 2.5 \(\upmu\)M, the results within 2 ROIs were outside of the limits of agreement on the corresponding Bland-Altman plots. In all eight cases, the automated cone counts were higher than the manual counts. Plausibly, the subtle qualitative differences in high magnification module images might require adjusted high-pass filter  
Figure 4: Median cone counts at 3, 6 and 9 degrees in healthy subjects (**A**) and CACD patients (**B**), according to human graders and automated high-pass filtered counts. Gray punctate areas indicate the range of cone counts according to human graders, and red upper and lower segmented lines represent the range of cone counts according to high-pass filtered cone counts. settings or the application of additional filters to constrain distinct noise-induced artifacts in order to prevent false-positive \"cone\" identification by a computer [24; 25]. Uncertain variation in qualitative characteristics of high magnification module images might require fuzzy logic-based preprocessing techniques to further ameliorate cone detection prior to the improvement of image quality grading parameters [26; 27]. The development of refined, objective high magnification module image quality grading parameters should result in a table of the figure where operators can easily identify the filter settings best used under predefined image and instrument characteristics [28]. Expected cone count should be incorporated as one of the image parameters for the applied Gaussian blur filter. In this scenario, the expected cone count represents the amount of signal present in an image, and other retinal structures represent noise. Until large high magnification module databases are available, the expected cone count may be derived from AO and histological studies or by manually counting a selection of representative ROIs. Various image parameters influence the expected cone count. In the healthy retina, the number of cones per square millimeter decreases with increasing distance from the macula [29]. Therefore, if a constant cone signal is warranted, the size of analyzed ROIs should be correspondingly adjusted to the distance from the macula. Inversely to cone density, the average cone spacing increases in the periphery [21]. While approximate cone spacing at the macula may be 5 microns, it increases up to roughly 8 microns at 5 degrees eccentricity, which is about 1.6 times the macular value [30]. Therefore, it is important to accordingly adapt the span of the sigma radius in a Gaussian blur filter to avoid positive signals that may be falsely recorded as cone signals [24]. The identified Gaussian sigma radius of 1.5 \(\upmu\)M should, for this reason, presumably be adapted if our high-pass filter is utilized to identify cones located closer than \(\sim\)3 degrees or beyond \(\sim\)9 degrees from the fovea in healthy eyes. In diseases affecting the macula, the pathological reduction in cones can induce a spatial increase between residual macular cones [31]. Thus, a corresponding adaptation of the Gaussian sigma in pathological conditions such as CACD may be required to obtain a reliable cone count. As mentioned, the reduction in cones hypothetically affects the amount of signal in an image.