A genetically optimized level set approach to segmentation of thyroid ultrasound images

Authors:
Dimitris K. Iakovidis;Michalis A. Savelonas;Stavros A. Karkanis;Dimitris E. Maroulis
Affiliations:
Dept. of Informatics and Telecommunications, University of Athens, Athens, Greece 15784;Dept. of Informatics and Telecommunications, University of Athens, Athens, Greece 15784;Dept. of Informatics and Computer Technology, Technological Educational Institute of Lamia, Lamia, Greece 35100;Dept. of Informatics and Telecommunications, University of Athens, Athens, Greece 15784
Venue:
Applied Intelligence
Year:
2007

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Abstract

This paper presents a novel framework for thyroid ultrasound image segmentation that aims to accurately delineate thyroid nodules. This framework, named GA-VBAC incorporates a level set approach named Variable Background Active Contour model (VBAC) that utilizes variable background regions, to reduce the effects of the intensity inhomogeneity in the thyroid ultrasound images. Moreover, a parameter tuning mechanism based on Genetic Algorithms (GA) has been considered to search for the optimal VBAC parameters automatically, without requiring technical skills. Experiments were conducted over a range of ultrasound images displaying thyroid nodules. The results show that the proposed GA-VBAC framework provides an efficient, effective and highly objective system for the delineation of thyroid nodules.