3D video-fluoroscopy can accurately estimate in-vivo kinematics of joint prosthesis. To this aim, for each of the hundreds of frames of an acquisition, a 3D surface model of the prosthesis is registered to the relevant contours on the 2D X-ray projections. Commercial software only provide simple edge detector followed by a time consuming manual procedure to delete the undesired contours. A fast and robust semi-automated prosthesis segmentation method, combining region growing and level set methods, is proposed to speed up the analysis and to reduce the human interaction.
Tersi L., Tarroni G., Corsi C., Stagni R. (2010). Automatic prosthesis segmentation in 3D fluoroscopy. SCICLI (RG) : s.n.
Automatic prosthesis segmentation in 3D fluoroscopy
TERSI, LUCA;TARRONI, GIACOMO;CORSI, CRISTIANA;STAGNI, RITA
2010
Abstract
3D video-fluoroscopy can accurately estimate in-vivo kinematics of joint prosthesis. To this aim, for each of the hundreds of frames of an acquisition, a 3D surface model of the prosthesis is registered to the relevant contours on the 2D X-ray projections. Commercial software only provide simple edge detector followed by a time consuming manual procedure to delete the undesired contours. A fast and robust semi-automated prosthesis segmentation method, combining region growing and level set methods, is proposed to speed up the analysis and to reduce the human interaction.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.