Comparing axial CT slices in quantized N-dimensional SURF descriptor space to estimate the visible body region.

In this paper, a method is described to automatically estimate the visible body region of a computed tomography (CT) volume image. In order to quantify the body region, a body coordinate (BC) axis is used that runs in longitudinal direction. Its origin and unit length are patient-specific and depend on anatomical landmarks. The body region of a test volume is estimated by registering it only along the longitudinal axis to a set of reference CT volume images with known body coordinates. During these 1D registrations, an axial image slice of the test volume is compared to an axial slice of a reference volume by extracting a descriptor from both slices and measuring the similarity of the descriptors. A slice descriptor consists of histograms of visual words. Visual words are code words of a quantized feature space and can be thought of as classes of image patches with similar appearance. A slice descriptor is formed by sampling a slice on a regular 2D grid and extracting a Speeded Up Robust Features (SURF) descriptor at each sample point. The codebook, or visual vocabulary, is generated in a training step by clustering SURF descriptors. Each SURF descriptor extracted from a slice is classified into the closest visual word (or cluster center) and counted in a histogram. A slice is finally described by a spatial pyramid of such histograms. We introduce an extension of the SURF descriptors to an arbitrary number of dimensions (N-SURF). Here, we make use of 2-SURF and 3-SURF descriptors. Cross-validation on 84 datasets shows the robustness of the results. The body portion can be estimated with an average error of 15.5mm within 9s. Possible applications of this method are automatic labeling of medical image databases and initialization of subsequent image analysis algorithms.