title: Optimisation of shape kernel and threshold in image-processing motion analysers.
abstract: The aim of the work is to optimise the image processing of a motion analyser. This is to improve accuracy, which is crucial for neurophysiological and rehabilitation applications. A new motion analyser, ELITE-S2, for installation on the International Space Station is described, with the focus on image processing. Important improvements are expected in the hardware of ELITE-S2 compared with ELITE and previous versions (ELITE-S and Kinelite). The core algorithm for marker recognition was based on the current ELITE version, using the cross-correlation technique. This technique was based on the matching of the expected marker shape, the so-called kernel, with image features. Optimisation of the kernel parameters was achieved using a genetic algorithm, taking into account noise rejection and accuracy. Optimisation was achieved by performing tests on six highly precise grids (with marker diameters ranging from 1.5 to 4 mm), representing all allowed marker image sizes, and on a noise image. The results of comparing the optimised kernels and the current ELITE version showed a great improvement in marker recognition accuracy, while noise rejection characteristics were preserved. An average increase in marker co-ordinate accuracy of +22% was achieved, corresponding to a mean accuracy of 0.11 pixel in comparison with 0.14 pixel, measured over all grids. An improvement of +37%, corresponding to an improvement from 0.22 pixel to 0.14 pixel, was observed over the grid with the biggest markers.
authors.affiliation: Bioengineering Department, Politecnico di Milano, Milan, Italy. pedrocchi@biomed.polimi.it
authors.forename: A
authors.initials: A
authors.lastname: Pedrocchi
authors.forename: G
authors.initials: G
authors.lastname: Baroni
authors.forename: S
authors.initials: S
authors.lastname: Sada
authors.forename: E
authors.initials: E
authors.lastname: Marcon
authors.forename: A
authors.initials: A
authors.lastname: Pedotti
authors.forename: G
authors.initials: G
authors.lastname: Ferrigno
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D008957:Models, Genetic; D009038:Motion