A Survey on Diagnosis of US Image Thyroid Nodules and Automated Classification
DOI:
https://doi.org/10.14419/ijet.v7i3.12.16112Published:
2018-07-20Keywords:
Thyroid disorders, Image processing, segmentation, classification, performance measures, Automated Diagnosis.Abstract
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] 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 thyÂroid 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 aspiraÂtion 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 InstrumentaÂtion 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 ultraÂsound 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
View Full Article:
How to Cite
License
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution Licensethat allows others to share the work with an acknowledgement of the work''s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal''s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Accepted 2018-07-23
Published 2018-07-20