We investigate the use of Legendre moments as biomarkers for an efficient and accurate classification of bone tissue on images coming from stem cell regeneration studies. Legendre moments are analysed from three different perspectives: (1) their discriminant properties in a wide set of preselected vectors of features based on our clinical and computational experience, providing solutions whose accuracy exceeds 90%; (2) the amount of information to be retained when using principal component analysis to reduce the dimensionality of the problem to either 2, 3, 4, 5 or 6 dimensions and (3) the use of the (α-β)-k-feature set problem to identify a k = 4 number of features which are more relevant to our analysis from a combinatorial optimisation approach. These techniques are compared in terms of computational complexity and classification accuracy to assess the strengths and limitations of the use of Legendre moments. The second contribution of this work goes to reduce the computational complexity by using graphics processing units (GPUs) and compute unified device architecture programming [Nvidia Developer Zone. 2014. CUDA. Available from: http:developer.nvidia.comobjectcuda.html]. We exploit single instruction multiple data parallelism and memory bandwidth on GPUs to accelerate the process to more than a 5 x factor on each 64 x 64 image tile. The result is a powerful combination of software and hardware methods which deliver a much faster response in contrast with existing solutions coming from conventional programming on multicore CPU platforms, thus offering a high performance alternative in clinical practice for real-time imaging.
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Vol. 4, Issue 3-4, p. 146-163