Reliable knowledge of in vivo joint kinematics is fundamental in clinical medicine. Fluoroscopic motion tracking theoretically permits a millimeter/degree level of accuracy in 3-D joint motion analysis, but the reliability of the local optimization algorithm [Levenberg-Marquardt (LMA)], typically used for the pose estimation, is highly operator dependent. A new memetic algorithm (MA), hybridizing global evolution and a local search metaphor for learning, is proposed to automate the analysis and improve its reliability and robustness. The performance of MA was assessed for in silico and in vivo elbow kinematics, with and without user supervision. The best learning strategy between Lamarckian and Baldwinian evolution was identified. MA's accuracy and repeatability was quantified and compared with LMA's. The algorithm performed best using a partial Lamarckian learning strategy. The geometric symmetry of analyzed bony segments influenced the accuracy, whereas the absolute bone pose with respect to the projection geometry affected the repeatability. In contrast to LMA, MA provided robust, repeatable, and operator independent pose estimations, even for in vivo analyses. The pose can be automatically estimated with errors lower than 1 mm and 1° for all the pose parameters except the depth position, if the investigated motion task avoids symmetric bony projection silhouettes.

Characterization of the performance of memetic algorithms for the automation of bone tracking with fluoroscopy

TERSI, LUCA;FANTOZZI, SILVIA;STAGNI, RITA
2015

Abstract

Reliable knowledge of in vivo joint kinematics is fundamental in clinical medicine. Fluoroscopic motion tracking theoretically permits a millimeter/degree level of accuracy in 3-D joint motion analysis, but the reliability of the local optimization algorithm [Levenberg-Marquardt (LMA)], typically used for the pose estimation, is highly operator dependent. A new memetic algorithm (MA), hybridizing global evolution and a local search metaphor for learning, is proposed to automate the analysis and improve its reliability and robustness. The performance of MA was assessed for in silico and in vivo elbow kinematics, with and without user supervision. The best learning strategy between Lamarckian and Baldwinian evolution was identified. MA's accuracy and repeatability was quantified and compared with LMA's. The algorithm performed best using a partial Lamarckian learning strategy. The geometric symmetry of analyzed bony segments influenced the accuracy, whereas the absolute bone pose with respect to the projection geometry affected the repeatability. In contrast to LMA, MA provided robust, repeatable, and operator independent pose estimations, even for in vivo analyses. The pose can be automatically estimated with errors lower than 1 mm and 1° for all the pose parameters except the depth position, if the investigated motion task avoids symmetric bony projection silhouettes.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Tersi, Luca; Fantozzi, Silvia; Stagni, Rita
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/522312
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