SPICE: Superpixel Classification for Cell Detection and Counting

An algorithm for the localization and counting of cells in histopathological images is presented. The algorithm relies on the presegmentation of an image into a number of superpixels followed by two random forests for classification. The first random forest determines if there are any cells in the superpixels at its input and the second random forest provides the number of cells in the respective superpixel. The algorithm is evaluated on a bone marrow histopathological dataset. We argue that a single random forest is not sufficient to detect all the cells in the image while a cascade of classifiers achieves higher accuracy. The results compare favorably with the state of the art but with a lower computational cost.