Genetic Programing for Cephalometric Landmark Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Innes:thesis,
  author =       "Andrew Innes",
  title =        "Genetic Programing for Cephalometric Landmark
                 Detection",
  school =       "School of Aerospace, Mechanical and Manufacturing
                 Engineering, RMIT University",
  year =         "2007",
  address =      "Victoria, Australia",
  month =        "29 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://adt.lib.rmit.edu.au/adt/uploads/approved/adt-VIT20080221.123310/public/02whole.pdf",
  size =         "265 pages",
  abstract =     "The domain of medical imaging analysis has burgeoned
                 in recent years due to the availability and
                 affordability of digital radiographic imaging equipment
                 and associated algorithms and, as such, there has been
                 significant activity in the automation of the medical
                 diagnostic process. One such process, cephalometric
                 analysis, is manually intensive and it can take an
                 experienced orthodontist thirty minutes to analyse one
                 radiology image. This thesis describes an approach,
                 based on genetic programming, neural networks and
                 machine learning, to automate this process. A
                 cephalometric analysis involves locating a number of
                 points in an X-ray and determining the linear and
                 angular relationships between them. If the points can
                 be located accurately enough, the rest of the analysis
                 is straightforward.

                 The investigative steps undertaken were as follows:
                 Firstly, a previously published method, which was
                 claimed to be domain independent, was implemented and
                 tested on a selection of landmarks, ranging from easy
                 to very difficult. These included the menton, upper
                 lip, incisal upper incisor, nose tip and sella
                 landmarks. The method used pixel values, and pixel
                 statistics (mean and standard deviation) of
                 pre-determined regions as inputs to a genetic
                 programming detector. This approach proved
                 unsatisfactory and the second part of the investigation
                 focused on alternative handcrafted features sets and
                 fitness measures. This proved to be much more
                 successful and the third part of the investigation
                 involved using pulse coupled neural networks to replace
                 the handcrafted features with learned ones. The fourth
                 and final stage involved an analysis of the evolved
                 programs to determine whether reasonable algorithms had
                 been evolved and not just random artefacts learnt from
                 the training images.

                 A significant finding from the investigative steps was
                 that the new domain independent approach, using pulse
                 coupled neural networks and genetic programming to
                 evolve programs,ii was as good as or even better than
                 one using the handcrafted features. The advantage of
                 this finding is that little domain knowledge is
                 required, thus obviating the requirement to manually
                 generate handcrafted features. The investigation
                 revealed that some of the easy landmarks could be found
                 with 100percent accuracy while the accuracy of finding
                 the most difficult ones was around 78percent.

                 An extensive analysis of evolved programs revealed
                 underlying regularities that were captured during the
                 evolutionary process. Even though the evolutionary
                 process took different routes and a diverse range of
                 programs was evolved, many of the programs with an
                 acceptable detection rate implemented algorithms with
                 similar characteristics.

                 The major outcome of this work is that the method
                 described in this thesis could be used as the basis of
                 an automated system. The orthodontist would be required
                 to manually correct a few errors before completing the
                 analysis.",
}

Genetic Programming entries for Andrew Innes

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