Automatic Spine Identification in Abdominal CT Slices using Image Partition Forests

Stuart Golodetz, Irina Voiculescu and Stephen Cameron


The identification of key features (e.g. organs and tumours) in medical scans (CT, MRI, etc.) is a vital first step in many other image analysis applications, but it is by no means easy to identify such features automatically. Using statistical properties of image regions alone, it is not always possible to distinguish between different features with overlapping greyscale distributions. To do so, it helps to make use of additional knowledge that may have been acquired (e.g. from a medic) about a patient’s anatomy. One important form this external knowledge can take is localization information: this allows a program to narrow down its search to a particular region of the image, or to decide how likely a feature candidate is to be correct (e.g. it would be worrisome were the aorta identified as running through the middle of a kidney). To make use of this information, however, it is necessary to identify a suitable frame of reference in which it can be specified. This frame should ideally be based on rigid structures, e.g. the spine and ribs. In this paper, we present a method for automatically identifying cross-sections of the spine in image partition forests of axial abdominal CT slices as a first step towards defining a robust coordinate system for localization.

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University of Oxford