Computer-Aided Grading of Neuroblastic Differentiation: Multi-Resolution and Multi-Classifier Approach

In this paper, the development of a computer-aided system for the classification of grade of neuroblastic differentiation is presented. This automated process is carried out within a multi-resolution framework that follows a coarse-to-fine strategy. Additionally, a novel segmentation approach using the Fisher-Rao criterion, embedded in the generic expectation-maximization algorithm, is employed. Multiple decisions from a classifier group are aggregated using a two-step classifier combiner that consists of a majority voting process and a weighted sum rule using priori classifier accuracies. The developed system, when tested on 14,616 image tiles, had the best overall accuracy of 96.89%. Furthermore, multi-resolution scheme combined with automated feature selection process resulted in 34% savings in computational costs on average when compared to a previously developed single-resolution system. Therefore, the performance of this system shows good promise for the computer-aided pathological assessment of the neuroblastic differentiation in clinical practice.