Automatically designed machine vision system for the localization of CCA transverse section in ultrasound images
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @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