Abstract
Intensity-invariant local phase-based feature extraction techniques have been previously proposed for both soft tissue and bone surface localization in ultrasound. A key challenge with such techniques is optimizing the selection of appropriate filter parameters whose values are typically chosen empirically and kept fixed for a given image. In this paper we present a novel method for contextual parameter selection that is adaptive to image content. Our technique automatically selects the scale, bandwidth and orientation parameters of Log-Gabor filters for optimizing the local phase symmetry in ultrasound images. The proposed approach incorporates principle curvature computed from the Hessian matrix and directional filter banks in a phase scale-space framework. Evaluations performed on in vivo and in vitro data demonstrate the improvement in accuracy of bone surface localization compared to empirically set parameterization results.
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Hacihaliloglu, I., Abugharbieh, R., Hodgson, A., Rohling, R. (2009). Automatic Data-Driven Parameterization for Phase-Based Bone Localization in US Using Log-Gabor Filters. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_88
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DOI: https://doi.org/10.1007/978-3-642-10331-5_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10330-8
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