A Survey on Diagnosis of US Image Thyroid Nodules and Automated Classification

  • Authors

    • Mohsin Khan A
    • Anuj Jain
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16112
  • Thyroid disorders, Image processing, segmentation, classification, performance measures, Automated Diagnosis.
  • Different types of human diseases are detected by medical image analysis which plays an important role. Studies that are developed for automated thyroid cancer classification is reviewed in this paper, especially to analyze the benign and malignant thyroid nodules features and comparisons. Hypothyroidism, hyperthyroidism, goitre and thyroid nodules (benign/malignant) are thyroid disorders. Ultrasound imaging, CT, MR imaging, nuclear medicine (NM) with positron emission tomography (PET), single photon emission computed tomography (SPECT) are the different medical techniques used to identify and classify thyroid gland abnormalities. In order to enhance the diagnosis of thyroid disease, various image processing techniques applied to thyroid ultra sound images are reviewed here. Studies based on non-clinical features for thyroid nodules classification is also discussed and reviewed.

     

     

  • References

    1. [1] Sheeja Agustin A, S.SureshBabu, “Thyroid segmentation on US images: An Overviewâ€, International Journal of Emerging Technology and Advanced Engineering, Vol.02, Issue 12, pp 88-93, (Feb 2013).

      [2] Slough, C. M., Randolph, G. W. Workup of well-differentiated thy­roid carcinoma. Cancer Control 13, 99-105 (2006).

      [3] Baloch, Z. W., Fleisher, S., LiVolsi, V. A., Gupta, P. K. Diagnosis of “follicular neoplasmâ€: a gray zone in thyroid fine-needle aspira­tion cytology. DiagnCytopathol 26, 41-44 (2002). DOI: 10.1002/ dc.10043

      [4] Baskin, H. J., Duick, D. S. The endocrinologists’ view of ultrasound guidelines for fine needle aspiration. Thyroid 16, 207-208 (2006). DOI:10.1089/thy.2006.16.207

      [5] Ivanac, G., Brkljacic, B., Ivanac, K., Huzjan, R., Skreb, F., Cikara, I. Vascularisation of benign and malignant thyroid nodules: CD US evaluation. Ultraschall Med 28, 502-506 (2007).

      [6] JeetendraGochare, PallaviChoudhary, “Pixel measurement of thyroid gland by using ultrasound imagesâ€, International Journal of Engineering Sciences and Research Technology , Vol.05, Issue 7, pp. 883-888, (July 2016).

      [7] Park, C. S., Kim, S. H., Jung, S. L., Kang, B. J., Kim, J. Y., Choi, J. J., Sung, M. S., Yim, H. W., Jeong, S. H. Observer variability in the sonographic evaluation of thyroid nodules. J Clin Ultrasound 38, 287-293 (2010). DOI: 10.1002/jcu.20689

      [8] Hong, Y. J., Son, E. J., Kim, E. K., Kwak, J. Y., Hong, S. W., Chang, H. S. Positive predictive values of sonographic features of solid thyroid nodule. Clin Imaging 34, 127-133 (2010). DOI: 10.1016/ j.clinimag.2008.10.034

      [9] Acharya, U. R., Faust, O., Sree, S. V., Molinari, F., Garberoglio, R., Suri, J. S. Cost-effective and non-invasive automated benign and malignant thyroid lesion classification in 3D contrast-enhanced ultrasound using combination of wavelets and textures: A class of thyroscan algorithms. Technol Cancer Res Treat 10, 371-380 (2011).

      [10] Acharya, U. R., VinithaSree, S., Krishnan, M. M., Molinari, F., Garberoglio, R., Suri, J. S. Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of thyroscan systems. Ultrasonics 52, 508-520 (2012). DOI: 10.1016/j.ultras.2011.11.003

      [11] Ojala, T., Pietikainen, M., Maenpaa, T. Multiresolutiongray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24, 971-987 (2002). DOI: 10.1109/TPAMI.2002.1017623

      [12] Laws, K. I. Rapid Texture Identification. Image Processing for Missile Guidance. San Diego, Society of Photo-Optical Instrumenta­tion Engineers (1980).

      [13] Bilmes, J. A. A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian Mixture and Hidden Markov Models. International Computer Science Institute 1-13 (1998).

      [14] Vapnik, V. Statistical learning theory (Adaptive and Learning Systems for Signal Processing, Communications and Control Series). John Wiley & Sons, New York (1998).

      [15] Larose, D. T. KNN. In: Discovering Knowledge in Data: An Introduction to Data Mining, 1st Ed., pp. 90-106. New Jersey; Wiley Interscience (2004).

      [16] Specht, D. F. Probabilistic neural networks. J Neural Networks 3, 109-118 (1990). DOI: 10.1016/0893-6080(90)90049-Q

      [17] Larose, D. T. Decision Trees. In: Discovering Knowledge in Data: An Introduction to Data Mining, 1st Ed., pp. 108-126. New Jersey; Wiley Interscience (2004).

      [18] Freund, Y., Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. J Comp SystSci 55, 119-139 (1997). DOI: 10.1006/jcss.1997.1504

      [19] Han, J., Kamber, M. Data Mining: Concepts and Techniques. Morgan Kaufmann (2006).

      [20] Sugeno, M. Industrial Applications of fuzzy Control. Elsevier Science, New York (1985).

      [21] Cappelli, C., Castellano, M., Pirola, I., Cumetti, D., Agosti, B., Gandossi, E., AgabitiRosei, E. The predictive value of ultrasound findings in the management of thyroid nodules. QJM 100, 29-35 (2007). DOI: 10.1093/qjmed/hcl121

      [22] Chan, B. K., Desser, T. S., McDougall, I. R., Weigel, R. J., Jeffrey, R. B. Jr. Common and uncommon sonographic features of papillary thyroid carcinoma. J Ultrasound Med 22, 1083-1090 (2003).

      [23] Khoo, M. L., Asa, S. L., Witterick, I. J., Freeman, J. L. Thyroid calcification and its association with thyroid carcinoma. Head Neck 24, 651-655 (2002).

      [24] Papini, E., Guglielmi, R., Bianchini, A., Crescenzi, A., Taccogna, S., Nardi, F., Panunzi, C., Rinaldi, R., Toscano, V., Pacella, C. M. Risk of malignancy in nonpalpable thyroid nodules: Predictive value of ultrasound and color-doppler features. J ClinEndocrinolMetab 87, 1941-1946 (2002).

      [25] Algin, O., Algin, E., Gokalp, G., Ocakoglu, G., Erdogan, C., Saraydaroglu, O., Tuncel, E. Role of duplex power doppler ultra­sound in differentiation between malignant and benign thyroid nodules. Korean J Radiol 11, 594-602 (2010). DOI: 10.3348/ kjr.2010.11.6.594

      [26] Jang, M., Kim, S. M., Lyou, C. Y., Choi, B. S., Choi, S. I., Kim, J. H. Differentiating benign from malignant thyroid nodules: Comparison of 2- and 3-dimensional sonography. J Ultrasound Med 31, 197-204 (2012).

      [27] Rago, T., Di Coscio, G., Basolo, F., Scutari, M., Elisei, R., Berti, P., Miccoli, P., Romani, R., Faviana, P., Pinchera, A., Vitti, P. Combined clinical, thyroid ultrasound and cytological features help to predict thyroid malignancy in follicular and hupsilonrthle cell thyroid lesions: Results from a series of 505 consecutive patients. ClinEndocrinol (Oxf) 66, 13-20 (2007). DOI: 10.1111/j.1365-2265.2006.02677.x

      [28] Gul, K., Ersoy, R., Dirikoc, A., Korukluoglu, B., Ersoy, P. E., Aydin, R., Ugras, S. N., Belenli, O. K., Cakir, B. Ultrasonographic evaluation of thyroid nodules: comparison of ultrasonographic, cytological, and histopathological findings. Endocrine 36, 464-472 (2009). DOI: 10.1007/s12020-009-9262-3

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    Khan A, M., & Jain, A. (2018). A Survey on Diagnosis of US Image Thyroid Nodules and Automated Classification. International Journal of Engineering & Technology, 7(3.12), 384-387. https://doi.org/10.14419/ijet.v7i3.12.16112