Deconvolution systems rely heavily on expert knowledge and would benefit from approaches that capture this expert
knowledge. Fuzzy logic is an approach that is used to capture expert knowledge rules and produce outputs that range in
degree. This paper describes a fuzzy-deconvolution-system that integrates traditional Richardson-Lucy deconvolution
with fuzzy components. The system is intended for restoration of 3D widefield images taken under conditions of
refractive index mismatch. The system uses a fuzzy rule set for calculating sample refractive index, a fuzzy median
filter for inter-iteration noise reduction, and a fuzzy rule set for stopping criteria.
A multistage algorithm is presented, whose components are based upon maximum likelihood estimation (MLE). From
3D scanning laser ophthalmoscope (SLO) image data, the algorithm finds the positions of the two anatomical boundaries
of the eye's fundus that define the retina, which are the internal limiting membrane (ILM) and the retinal pigment
epithelium (RPE). he retinal thickness is then calculated by subtraction. Retinal thickness is useful for indicating,
assessing risk of, and following several diseases, including various forms of macular edema and cysts.
A complete system for object segmentation, counting, quantification, and tracking from microscopic images was implemented. We found that image deconvolution and reconstruction operations are essential to the success of any general-purpose segmentation algorithm and hence are of paramount importance for a counting and tracking software system. Wavelet-based image enhancement, background equalization, and noise suppression routines are the components in our novel general-purpose segmentation algorithm. Simple object recognition based on averages and preset tolerances suffices for most applications. As expected, boundary smoothing is important if watershed-based blob separation is to be used. One of the challenges of a general-purpose counting and tracking system is the need for a large number of object quantification components (features). In tracking we found that incorporating weighted features into an error function improves the accuracy over just the path coherence criterion and that evaluating correspondences over multiple time frames improves the accuracy over using only two consecutive time frames.