Abstract
Ultrasound imaging of thyroid gland provides the ability to acquire valuable information for medical diagnosis. This study presents a novel scheme for the analysis of longitudinal ultrasound images aiming at efficient and effective computer-aided detection of thyroid nodules. The proposed scheme involves two phases: a) application of a novel algorithm for the detection of the boundaries of the thyroid gland and b) detection of thyroid nodules via classification of Local Binary Pattern feature vectors extracted only from the area between the thyroid boundaries. Extensive experiments were performed on a set of B-mode thyroid ultrasound images. The results show that the proposed scheme is a faster and more accurate alternative for thyroid ultrasound image analysis than the conventional, exhaustive feature extraction and classification scheme.
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References
Welker, M.J., Orlov, D.: Thyroid Nodules. American Family Physician 67(3) (2003)
Mailloux, G.E., Bertrand, M., Stampfler, R.: Local Histogram Information Content Of Ultrasound B-Mode Echographic Texture. Ultrasound in Medicine and Biology 11(5), 743–750 (1985)
Wagner, R.F., Insana, M.F., Brown, D.G.: Unified approach to the detection and classification of speckle texture in diagnostic ultrasound. Opt. Eng. 25, 738–742 (1986)
Fellingham, L.L., Sommer, F.G.: Ultrasonic characterization of tissue structure in the in vivo human liver and spleen. IEEE Transactions Sonics and Ultrasonics 31(4), 418–428 (1984)
Smutek, D., Sara, R., Sucharda, P., Tjahjadi, T., Svec, M.: Image texture analysis of sonograms in chronic inflammations of thyroid gland. Ultrasound in Medicine and Biology 29(11), 1531–1543 (2003)
Skouroliakou, C., Lyra, M., Antoniou, A., Vlahos, L.: Quantitative image analysis in sonograms of the thyroid gland. Nuclear Instruments and Methods in Physics Research A 569, 606–609 (2006)
Iakovidis, D.K., Savelonas, M.A., Karkanis, S.A., Maroulis, D.E.: Segmentation of Medical Images with Regional Inhomogeneities. In: Proc. International Conference on Pattern Recognition (ICPR), vol. 2, pp. 279–282. IAPR, Hong Kong (2006)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29, 51–59 (1996)
9. Rumack, C.M., Wilson, S.R., Charboneau, J.W., Johnson, J.A.: Diagnostic Ultrasound. Mosby (2004), ISBN 0323020232
Pujol, O., Radeva, P.: Supervised texture classification for intravascular tissue characterization. In: Suri, J.S., Wilson, D., Laximinarayan, S. (eds.) Handbook of Biomedical Image Analysis, Segmentation Models Part B, vol. 2, Springer, Heidelberg (2005)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, London (1999)
Swain, M.J., Ballard, D.H.: Color indexing. IJCV 7(I), 11–32 (1991)
Tomimori, E.K., Camargo, R.Y.A., Bisi, H., Medeiros-Neto, G.: Combined ultrasonografhic and cytological studies in the diagnosis of thyroid nodules. Biochimie 81, 447–452 (1999)
Kaus, M.R., Warfield, S.K., Jolesz, F.A., Kikinis, R.: Segmentation of Meningiomas and Low Grade Gliomas in MRI. In: Taylor, C., Colchester, A. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 1999. LNCS, vol. 1679, pp. 1–10. Springer, Heidelberg (1999)
Weiss, G.M., Provost, F.: The effect of class distribution on classifier learning. Technical Report ML-TR-43. Dept. of Computer Science, Rudgers University (2001)
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Keramidas, E.G., Iakovidis, D.K., Maroulis, D., Karkanis, S. (2007). Efficient and Effective Ultrasound Image Analysis Scheme for Thyroid Nodule Detection. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_93
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DOI: https://doi.org/10.1007/978-3-540-74260-9_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74258-6
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