Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Real-Time Extraction of Carotid Artery Contours from Ultrasound Images
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Initialization method for grammar-guided genetic programming
Knowledge-Based Systems
Crossover and mutation operators for grammar-guided genetic programming
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Computer Methods and Programs in Biomedicine
Segmentation of ultrasound images of the carotid using RANSAC and cubic splines
Computer Methods and Programs in Biomedicine
Carotid ultrasound segmentation using DP active contours
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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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 localization 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 82.7%, which exceeded the current state of the art by 4% 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.