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ΤND: A Thyroid Nodule Detection System for Analysis of Ultrasound Images and Videos

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Abstract

In this paper, we present a computer-aided-diagnosis (CAD) system prototype, named TND (Thyroid Nodule Detector), for the detection of nodular tissue in ultrasound (US) thyroid images and videos acquired during thyroid US examinations. The proposed system incorporates an original methodology that involves a novel algorithm for automatic definition of the boundaries of the thyroid gland, and a novel approach for the extraction of noise resilient image features effectively representing the textural and the echogenic properties of the thyroid tissue. Through extensive experimental evaluation on real thyroid US data, its accuracy in thyroid nodule detection has been estimated to exceed 95%. These results attest to the feasibility of the clinical application of TND, for the provision of a second more objective opinion to the radiologists by exploiting image evidences.

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Acknowledgment

We would like to thank EUROMEDICA S.A., Greece for the provision of the medical images. We would also like to thank N. Dimitropoulos, M.D. and G. Legakis, M.D. for their continuous support and advice. This work was supported by the Greek General Secretariat of Research and Technology (25%), the European Social Fund (75%), and the private sector, under the framework of Measure 8.3 of E.P. Antagonistikotita—3rd European Support Framework—PENED 2003 (grant no. 03-ED-662).

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Authors and Affiliations

  1. Department of Informatics and Telecommunications, University of Athens, Panepistimiopolis, GR-15784, Athens, Greece

    Eystratios G. Keramidas & Dimitris Maroulis

  2. Department of Informatics and Computer Technology, Technological Educational Institute of Lamia, GR-35100, Lamia, Greece

    Dimitris K. Iakovidis

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  1. Eystratios G. Keramidas
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  2. Dimitris Maroulis
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  3. Dimitris K. Iakovidis
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Correspondence to Eystratios G. Keramidas.

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Keramidas, E.G., Maroulis, D. & Iakovidis, D.K. ΤND: A Thyroid Nodule Detection System for Analysis of Ultrasound Images and Videos. J Med Syst 36, 1271–1281 (2012). https://doi.org/10.1007/s10916-010-9588-7

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  • DOI: https://doi.org/10.1007/s10916-010-9588-7

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