Bone segmentation and fracture detection in ultrasound using 3D local phase features

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Abstract

3D ultrasound (US) is increasingly considered as a viable alternative imaging modality in computer-assisted orthopaedic surgery (CAOS) applications. Automatic bone segmentation from US images, however, remains a challenge due to speckle noise and various other artifacts inherent to US. In this paper, we present intensity invariant three dimensional (3D) local image phase features, obtained using 3D Log-Gabor filter banks, for extracting ridge-like features similar to those that occur at soft tissue/bone interfaces. Our contributions include the novel extension of 2D phase symmetry features to 3D and their use in automatic extraction of bone surfaces and fractured fragments in 3D US. We validate our technique using phantom, in vitro, and in vivo experiments. Qualitative and quantitative results demonstrate remarkably clear segmentations results of bone surfaces with a localization accuracy of better than 0.62mm and mean errors in estimating fracture displacements below 0.65mm, which will likely be of strong clinical utility. © 2008 Springer-Verlag Berlin Heidelberg.

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APA

Hacihaliloglu, I., Abugharbieh, R., Hodgson, A., & Rohling, R. (2008). Bone segmentation and fracture detection in ultrasound using 3D local phase features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5241 LNCS, pp. 287–295). https://doi.org/10.1007/978-3-540-85988-8_35

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