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

Issue Title: Special Issue on Computational Intelligence in Medicine and Biology. Guest Editors: George Magoulas and Georgios Dounias. This paper presents a novel framework for thyroid ultrasound image segmentation that aims to accurately delineate thyroid nodules. This framework, named GA-VBAC inco...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2007-12, Vol.27 (3), p.193-203
Main Authors: Iakovidis, Dimitris K, Savelonas, Michalis A, Karkanis, Stavros A, Maroulis, Dimitris E
Format: Article
Language:English
Subjects:
Genetic algorithms
Thyroid gland
Ultrasonic imaging
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Summary:Issue Title: Special Issue on Computational Intelligence in Medicine and Biology. Guest Editors: George Magoulas and Georgios Dounias. 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. [PUBLICATION ABSTRACT]
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-007-0066-y