Automatic vertebrae localization from CT scans using volumetric descriptors

Abstract

Abstract-The localization and identification of vertebrae in spinal CT images plays an important role in many clinical applications, such as spinal disease diagnosis, surgery planning, and post-surgery assessment. However, automatic vertebrae localization presents numerous challenges due to partial visibility, appearance similarity of different vertebrae, varying data quality, and the presence of pathologies. Most existing methods require prior information on which vertebrae are present in a scan, and perform poorly on pathological cases, making them of little practical value. In this paper we describe three novel types of local information descriptors which are used to build more complex contextual features, and train a random forest classifier. The three features are progressively more complex, systematically addressing a greater number of limitations of the current state of the art.

Review of Automatic Microexpression Recognition in the Past Decade

“…A local binary pattern (LBP) is a descriptor originally proposed to describe local appearance in an image. The key idea behind it is that the relative brightness of neighbouring pixels can be used to describe local appearance in a geometrically and photometrically robust manner [14][15][16]. The basic LBP feature extractor relies on two free parameters, call them R and P. Uniformly sampling P points on the circumference of a circle with the radius R centred at a pixel, and taking their brightness relative to the centre pixel (brighter than, or not-one bit of information) allows the neighbourhood to be characterized by a P-bit number.…”