Prediction of relative position of CT slices using a computational intelligence system

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Mauro Castelli and Leonardo Trujillo and 
                 Leonardo Vanneschi and Ales Popovic",
  title =        "Prediction of relative position of {CT} slices using a
                 computational intelligence system",
  journal =      "Applied Soft Computing",
  volume =       "46",
  pages =        "537--542",
  year =         "2016",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2015.09.021",
  URL =          "",
  abstract =     "One of the most common techniques in radiology is the
                 computerized tomography (CT) scan. Automatically
                 determining the relative position of a single CT slice
                 within the human body can be very useful. It can allow
                 for an efficient retrieval of slices from the same body
                 region taken in other volume scans and provide useful
                 information to the non-expert user. This work addresses
                 the problem of determining which portion of the body is
                 shown by a stack of axial CT image slices. To tackle
                 this problem, this work proposes a computational
                 intelligence system that combines semantics-based
                 operators for Genetic Programming with a local search
                 algorithm, coupling the exploration ability of the
                 former with the exploitation ability of the latter.
                 This allows the search process to quickly converge
                 towards (near-)optimal solutions. Experimental results,
                 using a large database of CT images, have confirmed the
                 suitability of the proposed system for the prediction
                 of the relative position of a CT slice. In particular,
                 the new method achieves a median localization error of
                 3.4 cm on unseen data, outperforming standard Genetic
                 Programming and other techniques that have been applied
                 to the same dataset. In summary, this paper makes two
                 contributions: (i) in the radiology domain, the
                 proposed system outperforms current state-of-the-art
                 techniques; (ii) from the computational intelligence
                 perspective, the results show that including a local
                 searcher in Geometric Semantic Genetic Programming can
                 speed up convergence without degrading test
  keywords =     "genetic algorithms, genetic programming, Computerized
                 tomography, Radiology, Semantics, Local search",

Genetic Programming entries for Mauro Castelli Leonardo Trujillo Leonardo Vanneschi Ales Popovic