Figure 3: Schematic for filter-bank construction and BoW based classifier using Gabor descriptors. In the training step, the Gabor features are extracted foreach nucleus using Gabor filter bank from the result of optimization algorithm. Then, the features are clustered via k-means to identify the cluster centroids as visual words. The feature space is mapped to the index of the nearest visual word. After that, the bag of visual words forming the histogram model for each nucleus. In the final step, random forests classifier performed by using the histogram as an input to generate decision trees. Classification of a new nucleus involves first constructing the corresponding visual word signature and then using model of random forests to classify normal and cancer cell.