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

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@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

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