An Efficient 3D Segmentation Method for Spinal Canal Applied to CT Volume Sequence Data

An Efficient 3D Segmentation Method for Spinal Canal Applied to CT Volume Sequence Data

S. Zimeras (University of the Aegean, Greece)
Copyright: © 2012 |Pages: 10
DOI: 10.4018/ijrqeh.2012010104
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Abstract

With modern treatment planning techniques, the accurate definition of the target volume as well as the organs at risk is a crucial step for the treatment outcome. One of the key organs that must be protected during the irradiation treatment is the spinal canal. Nowadays, high resolution computed tomography (CT) data are required to perform accurate treatment planning, and there is demand for quick but accurate segmentation tools. In this work, a very simple approach that can accurately extract the spinal canal in three dimensions (3D) from CT images is presented. The user must define only the starting point for the algorithm, and the rest of the process is performed automatically. The core of the method is a boundary-tracing algorithm combined with linear interpolation techniques in the longitudinal (z) direction.
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Statistical Shape Analysis

Random fields are stochastic processes whose arguments vary continuously over some subset of Rn n-dimensional Euclidean space. They can be strictly defined on a measure space (Ω,F,P) where Ω is a set with generic element!, F F is a σ_-algebra of subsets of , and P is a probability measure on F satisfying the following axioms:

  • (1)

    0<P(A)<1 1 and P(Ω)=1

  • (2)

    if ; and 0 is the empty set.

    • Definition 1: A second order random field over is a function

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