Automatic Data-Driven Parameterization for Phase-Based Bone Localization in US Using Log-Gabor Filters

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 LogGabor 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.