Abstract
Low signal-to-noise ratio, imaging artifacts and bone boundaries appearing several millimeters in thickness have hampered the success of ultrasound (US) guided computer assisted orthopedic surgery procedures. In this paper we propose a robust and accurate bone localization method. The proposed approach is based on the enhancement of bone surfaces using the combination of three different local image phase features. The extracted local phase image features are used as an input to an \(L_{1}\) norm-based contextual regularization method for the enhancement of bone shadow regions. During the final stage the enhanced bone features and shadow region information is combined into a dynamic programming solution for the localization of the bone surface data. Qualitative and quantitative validation was performed on 150 in vivo US scans obtained from seven subjects by scanning femur, knee, distal radius and vertebrae bones. Validation against expert segmentation achieved a mean surface localization error of 0.26 mm a 67% improvement over state of the art.
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Hacihaliloglu, I. (2018). Localization of Bone Surfaces from Ultrasound Data Using Local Phase Information and Signal Transmission Maps. In: Glocker, B., Yao, J., Vrtovec, T., Frangi, A., Zheng, G. (eds) Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2017. Lecture Notes in Computer Science(), vol 10734. Springer, Cham. https://doi.org/10.1007/978-3-319-74113-0_1
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DOI: https://doi.org/10.1007/978-3-319-74113-0_1
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