Abstract
Thyroid cancer is the common cancer which can be found mostly in women as compared to men around the world. Thyroid gland is a butterfly-shaped gland that is located around the voice box. Earlier, doctors used to evaluate thyroid cancer manually, but now they are using computer-aided diagnosis (CAD) system for automatic detection. As incidence rate of thyroid cancer is increasing day by day, therefore, a better technology is required for its earlier detection. There are different types of imaging modalities, such as magnetic resonance imaging (MRI), ultrasound (US), and computerized tomography (CT), which are utilized for early detection of diseases. This paper presents and discusses the major trends for an exhaustive overview of thyroid nodule detection, segmentation, classification, and feature extraction techniques. The approaches used in CAD are summarized with their advantages and disadvantages.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Endocrineweb Homepage: https://www.endocrineweb.com/conditions/thyroid/thyroid-nodules/. Last accessed 2018/11/15
Cancer Treatment Centers of America Homepage: www.cancercenter.com/cancer/. Last accessed 2018/11/17
Cancer.Net Homepage: https://www.cancer.net/cancer-types/thyroid-cancer/statistics. Last accessed 2018/11/21
Cancer Stat Facts Homepage: https://seer.cancer.gov/statfacts/html/thyro.html. Last accessed 2018/11/21
Slideshare Image Pre-processing: https://www.slideshare.net/ASHI14march/image-pre-processing. Last accessed 2018/11/22
Babu, J.J.J., Sudha, G.F.: Adaptive speckle reduction in ultrasound images using fuzzy logic on Coefficient of Variation. Biomed. Signal Process. Control 23, 93–103 (2016)
Babu, J.J.J., Sudha, G.F.: Non-subsampled contourlet transform based image denoising in ultrasound thyroid images using adaptive binary morphological operations. IET Comput. Vis. 8(6), 718–728 (2014)
Kim, M., Song, T.: Speckle reduction of ultrasound B-mode image using patch recurrence. In: International Conference on Biomedical Engineering and Systems, pp. 1–5 (2016)
Narayan, N.S.: Speckle patch similarity for echogenicity-based multiorgan segmentation in ultrasound images of the thyroid gland. IEEE J. Biomed. Health Inf. 21(1), 172–183 (2017)
Kang, J., Lee, J.Y., Yoo, Y.: A new feature-enhanced speckle reduction method based on multiscale analysis for ultrasound b-mode imaging. IEEE Trans. Biomed. Eng. 63(6), 1178–1191 (2016)
Koundal, D., Gupta, S., Singh, S.: Nakagami-based total variation method for speckle reduction in thyroid ultrasound images. Proc. Inst. Mech. Eng., Part H: J. Eng. Med. 230(2), 97–110 (2016)
Koundal, D., Gupta, S., Singh, S.: Speckle reduction method for thyroid ultrasound images in neutrosophic domain. IET Image Process. 10(2), 167–175 (2016)
Morin, R.: Motion estimation-based image enhancement in ultrasound imaging. Ultrasonics 60, 19–26 (2015)
Tsantis, Stavros.: Inter-scale wavelet analysis for speckle reduction in thyroid ultrasound images. Comput. Med. Imaging Graph. 31(3), 117–127 (2007)
Toonkum, P., Chinrungrueng, C.: Speckle reduction of ultrasound images based on locally regularized Savitzky-Golay filters. In: 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 1–5. IEEE (2015)
Huang, J., Yang, X.: Fast reduction of speckle noise in real ultrasound images. Signal Process. 93(4), 684–694 (2013)
Elyasi, I., Pourmina, M.A.: Reduction of speckle noise ultrasound images based on TV regularization and modified bayes shrink techniques. Optik-Int. J. Light Electr. Optics 127(24), 11732–11744 (2016)
Hacini, M., Hachouf, F., Djemal, K.: A new speckle filtering method for ultrasound images based on a weighted multiplicative total variation. Signal Process. 103, 214–229 (2014)
Nugroho, H.A., Nugroho, A., Choridah, L.: Thyroid nodule segmentation using active contour bilateral filtering on ultrasound images. In: International Conference on Quality in Research (QiR), pp. 43–46. IEEE (2015)
Chang, C.-Y., Hong, Y.-C., Tseng, C.: A neural network for thyroid segmentation and volume estimation in CT images. IEEE Comput. Intell. Mag. 6(4), 43–55 (2011)
Keerthivasan, A., Jaganath Babu, J., Sudha, G.F.: Speckle noise reduction in ultrasound images using fuzzy logic based on histogram and directional differences. In: International Conference on Communications and Signal Processing (ICCSP), pp. 499–503. IEEE (2013)
Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 11(11), 1260–1270 (2002)
Zhao, J., Zhang, L., Tian, H.: Segmentation of ultrasound images of thyroid nodule for assisting fine needle aspiration cytology. Health Inf. Sci. Syst. 1(1) (2013)
Babu, J.J.J., Sudha, G.F.: A density current modeled adaptive weighted average despeckling filter for ultrasound thyroid images. Indian J. Sci. Technol. 9(46), 1–11 (2016)
Guo, W., Wang, Y., Yu, J.: Ultrasound harmonic imaging with reducing speckle noise by an interlaced iterative frequency compounding approach. Biomed. Eng. Inf. 34–39 (2015)
Măluţan, R., Terebeş, R., Germain, C., Borda, M., Cîşlariu, M.: Speckle noise removal in ultrasound images using sparse code shrinkage. In: E-Health and Bioengineering Conference (EHB), pp. 1–4. IEEE (2015)
Maroulis, D.E., Savelonas, M.A., Iakovidis, D.K., Karkanis, S.A., Dimitropoulos, N.: Variable background active contour model for computer-aided delineation of nodules in thyroid ultrasound images. IEEE Trans. Inf. Technol. Biomed. 11(5), 537–543 (2007)
Tsantis, S., Dimitropoulos, N., Cavouras, D., Nikiforidis, G.: A hybrid multi-scale model for thyroid nodule boundary detection on ultrasound images. Comput. Methods Prog. Biomed. 84(3), 86–98 (2006)
Du, W, Sang, N.: An effective method for ultrasound thyroid nodules segmentation. International Symposium on Bioelectronics and Bioinformatics (ISBB), pp. 207–210. IEEE (2015)
Chang, C.-Y., Lei, Y.-F., Tseng, C.-H., Shih, S.-R.: Thyroid segmentation and volume estimation in ultrasound images. IEEE Trans. Biomed. Eng. 57(6), pp. 1348–1357 (2010)
Iakovidis, D.K., Savelonas, M.A., Karkanis, S.A., Maroulis, D.E.: A genetically optimized level set approach to segmentation of thyroid ultrasound images. Appl. Intell. 27(3), 193–203 (2007)
Koundal, D.: Texture-based image segmentation using neutrosophic clustering. IET Image Process. 11(8), 640–645 (2017)
Keramidas, E.G., Iakovidis, D.K., Maroulis, D., Karkanis, S.: Efficient and effective ultrasound image analysis scheme for thyroid nodule detection. In: International Conference Image Analysis and Recognition, pp. 1052–1060. Springer, Berlin, Heidelberg (2007)
Koundal, D., Gupta, S., Singh, S.: Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set. Appl. Soft Comput. 40, 86–97 (2016)
Savelonas, M.A., Iakovidis, D.K., Dimitropoulos, N., Maroulis, D.: Variable background active contour model for automatic detection of thyroid nodules in ultrasound images. IEEE Trans. Inf. Technol. Biomed. 11(5), 17–20 (2007)
Savelonas, M.A., Iakovidis, D.K., Dimitropoulos, N., Maroulis, D.: Computational characterization of thyroid tissue in the radon domain. Comput.-Based Med. Syst. 189–192 (2007)
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 Trans. Inf. Technol. Biomed. 13(4), 519–527 (2009)
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(3), 1271–1281 (2012)
Ma, J., Luo, S. Dighe, M., Lim, D.-J. Kim, Y.: Differential diagnosis of thyroid nodules with ultrasound elastography based on support vector machines. In: IEEE International Ultrasonics Symposium, pp. 1372–1375 (2010)
Chang, Chuan-Yu, Hsin-Cheng Huang, and Shao-Jer Chen.: Automatic thyroid nodule segmentation and component analysis in ultrasound images. Biomed. Eng.: Appl., Basis Commun. 22(2), 81–89 (2010)
Chang, C.-Y., Lei, Y.-F., Tseng, C.-H., Shih, S.-R.: Thyroid segmentation and volume estimation in ultrasound images. IEEE Trans. Biomed. Eng. 57(6), 1348–1357 (2010)
Ma, J., Wu, F., Zhao, Q., Kong, D.: Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks. Int. J. Comput. Assist. Radiol. Surg. 12(11), 1895–1910 (2017)
Tsantis, S., Glotsos, D., Kalatzis, G., Dimitropoulos, N., Nikiforidis, G., Cavouras, D.: Automatic contour delineation of thyroid nodules in ultrasound images employing the wavelet transform modulus-maxima chains. In: 1st International Conference from Scientific Computing to Computational Engineering, pp. 8–10 (2004)
Kollorz, E., Angelopoulou, E., Beck, M., Schmidt, D., Kuwert T.: Using power watersheds to segment benign thyroid nodules in ultrasound image data. In Bildverarbeitung für die Medizin 124–128 (2011)
Maroulis, D.E., Savelonas, M.A., Karkanis, S.A., Iakovidis, D.K., Dimitropoulos, N.: Computer-aided thyroid nodule detection in ultrasound images. Comput.-Based Med. Syst. 271–276 (2005)
Gireesha, H. M., S. Nanda.: Thyroid nodule segmentation and classification in ultrasound images. Int. J. Eng. Res. Technol. 2252–2256 (2014)
Ganesh, P., Babu, J.: Automated thyroid nodule segmentation algorithm for ultrasound images. Int. Conf. Signal Process. 3(3), 85–90 (2014)
Koundal, D., Gupta, S., Singh, S.: Computer aided thyroid nodule detection system using medical ultrasound images. Biomed. Signal Process. Control 40, 117–130 (2018)
Saiti, F., Naini, A.A., Shoorehdeli, M.A., Teshnehlab, M.: Thyroid disease diagnosis based on genetic algorithms using PNN and SVM. Bioinf. Biomed. Eng. 1–4 (2009)
Shukla, A., Tiwari, R., Kaur, P., Janghel, R.R.: Diagnosis of thyroid disorders using artificial neural networks. IEEE Adv. Comput. Conf. 1016–1020 (2009)
Keleş, A., Keleş, A.: ESTDD: expert system for thyroid diseases diagnosis. Expert Syst. Appl. 1, 242–246 (2008)
Polat, K., Şahan, S., Güneş, S.: A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis. Expert Syst. Appl. 32(4), 1141–1147 (2007)
Pechenizkiy, M., Tsymbal, A., Puuronen, S., Patterson, D.: Feature extraction for dynamic integration of classifiers. Fundamenta Informaticae 77(3), 243–275 (2007)
Singh, N., Jindal, A.: A segmentation method and comparison of classification methods for thyroid ultrasound images. Int. J. Comput. Appl. 50(11), 43–49 (2012)
Malathi, M., Srinivasan, S.: Classification of ultrasound thyroid nodule using feed forward neural network. World Eng. Appl. Sci. 8(1), 12–17 (2017)
Nugroho, H.A., Rahmawaty, M., Triyani, Y., Ardiyanto, I.: Texture analysis for classification of thyroid ultrasound images. In: Electronics Symposium (IES), pp. 476–480 (2016)
Chang, C-Y, Huang H.-C., Chen, S.-J.: Thyroid nodule segmentation and component analysis in ultrasound images. Biomed. Eng.: Appl., Basis Commun. 22(2), 910–917 (2009)
Selvathi, D., Sharnitha, V.S.: Thyroid classification and segmentation in ultrasound images using machine learning algorithms. In Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), pp. 836–841 (2011)
Chang, C.-Y., Huang, H.-C., Chen, S.-J.: Automatic thyroid nodule segmentation and component analysis in ultrasound images. Biomed. Eng.: Appl., Basis Commun. 22(2), 81–89 (2010)
Garg, H, Jindal, A.: Segmentation of thyroid gland in ultrasound image using neural network. In Computing, Communications and Networking Technologies (ICCCNT), pp. 1–5 (2013)
Kim, H.-C., Ghahramani, Z.: Bayesian Gaussian process classification with the EM-EP algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), pp. 1948–1959 (2006)
Amasyalı, S.A., Albayrak, F.S.: Fuzzy c-means clustering on medical diagnostic systems. In: International 12th Turkish Symposium Artificial intelligence and neural networks (2003)
Conflict of Interest
There is no conflict of interest.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Anand, V., Koundal, D. (2020). Computer-Assisted Diagnosis of Thyroid Cancer Using Medical Images: A Survey. In: Singh, P., Kar, A., Singh, Y., Kolekar, M., Tanwar, S. (eds) Proceedings of ICRIC 2019 . Lecture Notes in Electrical Engineering, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-030-29407-6_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-29407-6_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29406-9
Online ISBN: 978-3-030-29407-6
eBook Packages: EngineeringEngineering (R0)