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Survey of Computer-Aided Diagnosis of Thyroid Nodules in Medical Ultrasound Images

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Advances in Computing and Information Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 177))

Abstract

In medical science, diagnostic imaging is an invaluable tool because of restricted observation of the specialist and uncertainties in medical knowledge. A thyroid ultrasound is a non-invasive imaging study used to understand the anatomy of thyroid gland which is not possible with other techniques. Various classifiers are used to characterize thyroid nodules into benign/malignant based on the extracted features to make correct diagnosis. Current classification approaches are reviewed with classification accuracy for thyroid ultrasound image applications. The aim of this paper is to review existing approaches for the diagnosis of Nodules in thyroid ultrasound images.

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References

  1. Unnikrishnan, A.G., Menon, U.V.: Thyroid disorders in India: An epidemiological perspective. Indian Journal of Endocrinology and Metabolism 15, 78–81 (2011)

    Article  Google Scholar 

  2. Horvath, E., Majlis, S., Rossi, R., Franco, C., Niedmann, J.P., Castro, A.: An ultrasonogram reporting system for thyroid nodules stratifying cancer risk for clinical management. J. Clin. Endocrinol Metab, 748–751 (2009)

    Google Scholar 

  3. Baskin, H.J.: Thyroid Ultrasound and Ultrasound-Guided FNA, 2nd edn. Springer (2008)

    Google Scholar 

  4. Sharma, N., Aggarwal, L.M.: Automated medical image segmentation techniques. Jnl. of Medical Physics / Association of Medical Physicists of India 35(1), 3–14 (2010)

    Google Scholar 

  5. Pal, S.K.: A review on image segmentation techniques. Pattern Recg., 1277–1294 (1993)

    Google Scholar 

  6. Noble, J.A., Boukerroui, D.: Ultrasound image segmentation: A survey. IEEE Trans. on Medical Imaging 25, 987–1010 (2006)

    Article  Google Scholar 

  7. Ma, J., Luo, S., Dighe, M., Lim, D., Kim, Y.: Differential Diagnosis of Thyroid Nodules with Ultrasound Elastography based on Support Vector Machines. In: IEEE Int. Ultrasonics Symp. Proc, pp. 1372–1375 (2010)

    Google Scholar 

  8. Savelonas, M., Maroulis, D., Iakovidis, D., Karkanis, S.: A VBAC Model for Automatic Detection of Thyroid Nodules in Ultrasound Images, pp. 1–4. IEEE (2005)

    Google Scholar 

  9. Savelonas, M.A., Iakovidis, D.K., Dimitropoulos, N., Maroulis, D.: Computational Characterization of Thyroid Tissue in the Radon Domain. In: IEEE International Symposium on Computer-Based Medical Systems, pp. 1–4 (2007)

    Google Scholar 

  10. Savelonas, M.A., Maroulis, D.E., Iakovidis, D.K., Dimitropoulos, N.: Computer-Aided Malignancy Risk Assessment of Nodules in Thyroid US Images Utilizing Boundary Descriptors. In: Panhellenic Conf. on Informatics, pp. 156–160. IEEE (2008)

    Google Scholar 

  11. Savelonas, M.A., Iakovidis, D.K., Legakis, I., Maroulis, D.: Active Contours Guided by Echogenicity and Texture for Delineation of Thyroid Nodules in Ultrasound Images. IEEE Transactions on Information Technology in Biomedicine 13, 519–527 (2009)

    Article  Google Scholar 

  12. Chang, C., Lei, Y., Tseng, C., Shih, S.: Thyroid Segmentation and Volume Estimation in Ultrasound Images. In: IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 3442–3447 (2008)

    Google Scholar 

  13. Temurtas, F.: A comparative study on thyroid disease diagnosis using neural networks. Expert Systems with Applications 36, 944–949 (2009)

    Article  Google Scholar 

  14. Saiti, F., Naini, A.A., Shoorehdeli, M.A., Teshnehlab, M.: Thyroid Disease Diagnosis Based on Genetic Algorithms using PNN and SVM, pp. 1–4. IEEE (2009)

    Google Scholar 

  15. Cai, J., Liu, Z.Q.: Pattern recognition using Markov random field models. Patt. Recog., 725–733 (2002)

    Google Scholar 

  16. Cheng, H.D., Shan, J., Ju, W., Guo, Y., Zhang, L.: Automated breast cancer detection and classification using ultrasound images: A survey. Pattern Recognition 43, 299–317 (2010)

    Article  MATH  Google Scholar 

  17. Zhang, G., Berardi, V.L.: An investigation of neural networks in thyroid function diagnosis. Health Care Management Science 1, 29–37 (1998)

    Article  Google Scholar 

  18. Shukla, A., Kaur, P., Tiwari, R., Janghel, R.R.: Diagnosis of Thyroid Disorders using Artificial Neural Networks. In: IEEE Int. Advance Computing Conf., pp. 1016–1020 (2009)

    Google Scholar 

  19. Rouhani, M., Mansouri, K.: Comparison of several ANN architectures on the Thyroid diseases grades diagnosis. In: Int. Comp. Science and IT- Spring Conf., pp. 526–528. IEEE (2009)

    Google Scholar 

  20. Ma, J., Luo, S., Dighe, M., Lim, D., Kim, Y.: Differential Diagnosis of Thyroid Nodules with Ultrasound Elastography based on Support Vector Machines. In: IEEE Int. Ultrasonics Symp. Proc., pp. 1372–1375 (2010)

    Google Scholar 

  21. Tsantis, S., Dimitropoulos, N., Cavouras, D., Nikiforidis, G.: A hybrid multi-scale model for thyroid nodule boundary detection on ultrasound images. Computer Methods and Programs in Biomedicine, 86–98 (2006)

    Google Scholar 

  22. Polat, K., Sahan, S., Gunes, S.: A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis. Expert Systems with Applications 32, 1141–1147 (2007)

    Article  Google Scholar 

  23. Seabra, J.C.R., Fred, A.L.N.: Towards the Development of a Thyroid Ultrasound Biometric Scheme Based on Tissue Echo-morphological Features. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOSTEC 2009. CCIS, vol. 52, pp. 286–298. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  24. Selvathi, D., Sharnitha, V.S.: Thyroid Classification and Segmentation in Ultrasound Images Using Machine Learning Algorithms. In: Proc. of Int. Conf. on Signal Processing, Communication, Computing and Networking Technologies, pp. 836–841. IEEE (2011)

    Google Scholar 

  25. Dogantekin, E., Dogantekin, A., Derya, A.: An expert system based on Generalized Discriminant Analysis and Wavelet Support Vector Machine for diagnosis of thyroid diseases. Expert Systems with Applications 38, 146–150 (2011)

    Article  Google Scholar 

  26. Kodaz, H., Seral, O., Arslan, A., Salih, G.: Medical application of information gain based AIRS: Diagnosis of thyroid disease. Expert Systems with Applications, 3086–3092 (2009)

    Google Scholar 

  27. Keles, A.: ESTDD: Expert system for thyroid diseases diagnosis. Expert Systems with Applications, 242–246 (2008)

    Google Scholar 

  28. Polat, K., Sahan, S., Gunes, S.: A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis. Expert Systems with Applications 32, 1141–1147 (2007)

    Article  Google Scholar 

  29. Pechenizkiy, M., Tsymbal, A., Puuronen, S., Patterson, D.W.: Feature extraction for dynamic integration of classifiers. Fundamenta Informaticae 77(3), 243–275 (2007)

    MathSciNet  MATH  Google Scholar 

  30. Sierra, A.: High order Fisher’s discriminants. Pattern Recogn. 35, 1291–1302 (2002)

    Article  MATH  Google Scholar 

  31. Sierra, A., Echeverria, A.: Evolutionary Discriminant Analysis. IEEE Trans. Evolutionary Computation 10(1) (February 2006)

    Google Scholar 

  32. Hassan, R., Nath, B., Kirley, M.: A data clustering algorithm based on single hidden markov model. In: Proceedings of the International Multiconference on Computer Science and Information Technology, pp. 57–66 (2006)

    Google Scholar 

  33. Kim, H.C., Ghahramani, Z.: Bayesian Gaussian process classification with the EM-EP algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 1948–1959 (2006)

    Article  Google Scholar 

  34. Pasi, L.: Similarity classifier applied to medical data sets. In: Int. Conf. on Soft Computing, 10 Sivua, Fuzziness in Finland 2004, Helsinki, Finland & Gulf of Finland & Tallinn, Estonia (2004)

    Google Scholar 

  35. Myles, A.J., Brown, S.D.: Decision pathway modeling. Jnl. of Chemometrics, 286–293 (2004)

    Google Scholar 

  36. Raymer, M.L.: Knowledge Discovery in Biological Dataset Using a Hybrid Bayes classifier/Evolutionary Algorithm. IEEE Trans. on Bioinformatics and Bioengineering (2003)

    Google Scholar 

  37. Ozyılmaz, L., Yıldırım, T.: Diagnosis of thyroid disease using artificial neural network methods. In Proc.of Int. Conf. on Neural Information Processing 4, 2033–2036 (2002)

    Google Scholar 

  38. Duch, W., Adamczack, R., Grabczewski, K.: A new methodology of extraction, optimization and application of crisp and fuzzy logical rules. IEEE Trans. Neural Net. 12, 277–306 (2001)

    Article  Google Scholar 

  39. Cheong, T.S., Yoon, C.H.: A memory based class recursive partition averaging. IEEE Tencon, 1038–1041 (1999)

    Google Scholar 

  40. Abe, S., Thawonmas, R.: A fuzzy classifier with Ellipsoidal regions. IEEE Trans. Fuzzy Syst. 5(3) (August 1997)

    Google Scholar 

  41. Serpen, G., Jiang, H., Allred, L.: Performance analysis of probabilistic potential function neural network classifier. In: Proc. of Artificial Neural Netw. in Engg. Conf., vol. 7, pp. 471–476 (1997)

    Google Scholar 

  42. Mailloux, G.C., Bertranti, M.: Texture Analysis Of Ultrasound B-Mode Images By Segmentation. Ultrasonic Imaging 6, 262–277 (1984)

    Article  Google Scholar 

  43. Morifuji, H.: Analysis of ultrasound B-mode histogram in thyroid tumors. Nippon Geka Gakkai Zasshi 90(2), 210–221 (1989)

    Google Scholar 

  44. Hirning, T., Zuna, I., Schlaps, D.: Quantification and classification of echographic findings the thyroid gland by computerized b-mode texture analysis. Eur. J. Radiol. 9, 244–247 (1989)

    Google Scholar 

  45. Smutek, D., Šara, R., Sucharda, P., Tjahjadi, T., Švec, M.: Image texture analysis of sonograms in chronic inflammations of thyroid gland. Ultrasound Med. Biol. 29, 1531–1543 (2003)

    Article  Google Scholar 

  46. Keramidas, E.G., Iakovidis, D.K., Maroulis, D., Dimitropoulos, N.: Thyroid Texture Representation via Noise Resistant Image Features. In: IEEE Int. Symp. on Comp. Based Med. Sys., pp. 560–565 (2008)

    Google Scholar 

  47. Keramidas, E.G., Maroulis, D., Iakovidis, D.K.: TND: A Thyroid Nodule Detection System for Analysis of Ultrasound Images and Videos. J. Med. Syst., 1–11 (2010)

    Google Scholar 

  48. Chen, Y., Hou, C., Lee, M., Chen, S., Tsai, Y., Hsu, T.: The Image Feature Analysis for Microscopic Thyroid Tissue Classification. In: 30th Annual Int. IEEE EMBS Conf., 4059–4062 (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. UIET, Panjab University, Chandigarh, India

    Deepika Koundal, Savita Gupta & Sukhwinder Singh

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  1. Deepika Koundal
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  2. Savita Gupta
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  3. Sukhwinder Singh
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Editors and Affiliations

  1. , Department of Computer Science, Jackson State University, John R. Lynch Street 1400, Jackson, 39217, USA

    Natarajan Meghanathan

  2. Wireilla Net Solutions PTY Ltd, Melbourne, Australia

    Dhinaharan Nagamalai

  3. Department of Computer Science & Eng., University of Calcutta, Calcutta, 700 073, India

    Nabendu Chaki

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Koundal, D., Gupta, S., Singh, S. (2013). Survey of Computer-Aided Diagnosis of Thyroid Nodules in Medical Ultrasound Images. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_47

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  • DOI: https://doi.org/10.1007/978-3-642-31552-7_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31551-0

  • Online ISBN: 978-3-642-31552-7

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