“…Their aim is to delineate cell borders using such techniques as: local greyscale thresholding followed by scissoring and morphological thinning [4], [5], scale-space filtering followed by binarization and morphological processing [6] or hexagon detection using shape dependent filters [7], [8], [9]. More sophisticated methods include application of watersheds [10], [11], [12], [13], [14], active contours [15], [16], genetic algorithms [17] or analysis of local pixel levels aimed at finding intensity valleys corresponding to borders between cells [18]. Several machine learning approaches have also been proposed by the team of Ruggeri, including: neural network [19], [20], Bayesian framework [21], support vector machines classifier [22] and genetic algorithm [23].…”