BACKGROUND: Accurate localization of bone surfaces remains a challenge hampering adoption of ultrasound guidance in computer-assisted orthopaedic surgery. Local phase image features have recently been proven efficacious for segmenting bone surfaces from ultrasound images, but the quality of the processing depends on numerous filter parameters that are currently set through a trial and error process that is tedious, unintuitive and subject to large inter-user variability. METHODS: A method is presented for automatically selecting parameters of Log-Gabor filters used to extract bone surfaces from 3D ultrasound volumes that is based on properties estimated directly from the specific image. RESULTS: A 15% and 69% average improvement in bone surface localization accuracy on phantom and clinical data, respectively, is demonstrated compared with empirically-set parameters. CONCLUSIONS: These findings imply that Log-Gabor filter parameter optimization is necessary for accurate extraction of bone surfaces from ultrasound data.
BACKGROUND: Accurate localization of bone surfaces remains a challenge hampering adoption of ultrasound guidance in computer-assisted orthopaedic surgery. Local phase image features have recently been proven efficacious for segmenting bone surfaces from ultrasound images, but the quality of the processing depends on numerous filter parameters that are currently set through a trial and error process that is tedious, unintuitive and subject to large inter-user variability. METHODS: A method is presented for automatically selecting parameters of Log-Gabor filters used to extract bone surfaces from 3D ultrasound volumes that is based on properties estimated directly from the specific image. RESULTS: A 15% and 69% average improvement in bone surface localization accuracy on phantom and clinical data, respectively, is demonstrated compared with empirically-set parameters. CONCLUSIONS: These findings imply that Log-Gabor filter parameter optimization is necessary for accurate extraction of bone surfaces from ultrasound data.
Authors: Mikael Brudfors; Alexander Seitel; Abtin Rasoulian; Andras Lasso; Victoria A Lessoway; Jill Osborn; Atsuto Maki; Robert N Rohling; Purang Abolmaesumi Journal: Int J Comput Assist Radiol Surg Date: 2015-04-18 Impact factor: 2.924
Authors: Zian Fanti; Fabian Torres; Eric Hazan-Lasri; Alfonso Gastelum-Strozzi; Leopoldo Ruiz-Huerta; Alberto Caballero-Ruiz; F Arámbula Cosío Journal: J Healthc Eng Date: 2018-06-03 Impact factor: 2.682