Each colony region was estimated for ten morphological features; namely, area, perimeter, centroid, equivalent diameter, eccentricity, solidity, major axis, minor axis, extent, and orientation. The definitions of these features are described in S1 Table. The textural features adopted in this study can be explained in terms of a gray-level co-occurrence matrix of 13 features (for details, see ), which reveals the different combinations of pixel intensity values in a specific spatial displacement. The most relevant features of colony categories are identified using the feature selection technique. In this study, Fisher score analysis was applied to determine the most relevant features for the subsequent classification task, while excluding the irrelevant ones. Fisher scores were automatically computed for each feature in the feature sets of training data (Fig 4) and used to select the informative features by which the within-class distance is minimized and the between-class distance is maximized . Specifically, given the selected f features, the input data matrix X ∊ Ra×n reduces to Q ∊ Rm×n. Hence, the Fisher score is represented as follows: argmaxtrQ{V˜t−1V˜b}(6) where V~t and V~b are defined as V˜t=∑i=1n(Qi−μ~)(Qi−μ~)L,V˜b=∑k=1cnk(μ~k−μ~)(μ~k−μ~)L(7) where μ~k and nk are the mean vector and size of the kth class, respectively, in the reduced data space; that is, Qμ˜=∑k=1cnkμ˜k is the overall mean vector of the reduced data.