Saliency-based rotation invariant descriptor for wrist detection in whole body CT images

In this paper, we propose a saliency-based rotation invariant descriptor and apply it to detect wrists in CT images. The descriptor is motivated by the observation that salient landmarks around wrists usually form a characteristic spatial configuration (Fig. 1). In our framework, a set of interest points are firstly computed via scale-space analysis. For each interest point, we compute a pyramid of scale-distance 2D histograms constructed with neighboring interest points. The descriptor represents the spatial configuration among neighboring interest points in a rotation-invariant fashion. A cascade set of random forests are trained to distinguish wrist from other anatomies using this descriptor. Our algorithm shows robust and accurate performance on 41 whole body CT scans with diverse context, orientations and articulation configurations.