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

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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.

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Authors and Affiliations

  1. Dept. of Informatics and Telecommunications, University of Athens, Panepistimiopolis, Ilisia, 15784, Athens, Greece

    Dimitris K. Iakovidis, Michalis A. Savelonas & Dimitris E. Maroulis

  2. Dept. of Informatics and Computer Technology, Technological Educational Institute of Lamia, 3rd kilometer, Old National Road, 35100, Lamia, Greece

    Stavros A. Karkanis

Authors
  1. Dimitris K. Iakovidis
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  2. Michalis A. Savelonas
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  3. Stavros A. Karkanis
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  4. Dimitris E. Maroulis
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Correspondence to Dimitris K. Iakovidis.

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Iakovidis, D.K., Savelonas, M.A., Karkanis, S.A. et al. A genetically optimized level set approach to segmentation of thyroid ultrasound images. Appl Intell 27, 193–203 (2007). https://doi.org/10.1007/s10489-007-0066-y

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