Automatically designed machine vision system for the localization of CCA transverse section in ultrasound images

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{Benes201392,
  author =       "Radek Benes and Jan Karasek and Radim Burget and 
                 Kamil Riha",
  title =        "Automatically designed machine vision system for the
                 localization of CCA transverse section in ultrasound
                 images",
  journal =      "Computer Methods and Programs in Biomedicine",
  volume =       "109",
  number =       "1",
  pages =        "92--103",
  year =         "2013",
  keywords =     "genetic algorithms, genetic programming, Common
                 carotid artery, Localisation, Machine vision system",
  ISSN =         "0169-2607",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0169260712001964",
  DOI =          "doi:10.1016/j.cmpb.2012.08.014",
  size =         "12 pages",
  abstract =     "The common carotid artery (CCA) is a source of
                 important information that doctors can use to evaluate
                 the patients' health. The most often measured
                 parameters are arterial stiffness, lumen diameter, wall
                 thickness, and other parameters where variation with
                 time is usually measured. Unfortunately, the manual
                 measurement of dynamic parameters of the CCA is time
                 consuming, and therefore, for practical reasons, the
                 only alternative is automatic approach. The initial
                 localisation of artery is important and must precede
                 the main measurement. This article describes a novel
                 method for the localization of CCA in the transverse
                 section of a B-mode ultrasound image. The novel method
                 was designed automatically by using the grammar-guided
                 genetic programming (GGGP). The GGGP searches for the
                 best possible combination of simple image processing
                 tasks (independent building blocks). The best possible
                 solution is represented with the highest detection
                 precision. The method is tested on a validation
                 database of CCA images that was specially created for
                 this purpose and released for use by other scientists.
                 The resulting success of the proposed solution was
                 82.7percent, which exceeded the current state of the
                 art by 4percent while the computation time requirements
                 were acceptable. The paper also describes an automatic
                 method that was used in designing the proposed
                 solution. This automatic method provides a universal
                 approach to designing complex solutions with the
                 support of evolutionary algorithms.",
  notes =        "Brno University of Technology, Department of
                 Telecommunications, Purkynova 118, Brno, Czech
                 Republic",
}

Genetic Programming entries for Radek Benes Jan Karasek Radim Burget Kamil Riha

Citations