High Performance Computation of Moments for an Accurate Classification of Bone Tissue Images

This work assesses the role played by Zernike moments as descriptors of image tiles for its subsequent classification into bone and cartilage regions to quantify the degree of bone tissue regeneration from in-vivo stem cells implanted on animals. The characterization of those image tiles is performed through a vector of features, whose optimal composition is extensively analyzed after testing 19 subsets of Zernike moments selected as the best potential candidates. The computation of those moments, together with the subsequent classifying process, is then accelerated on graphics processing units (GPUs) with remarkable speed-up factors up to 20x for Zernike moments and up to 70x for the classifiers. Overall, we provide a tool for an efficient image characterization and optimal classification into regions, which is boosted using GPUs to enable real-time processing for our set of input biomedical images.