Diagnosis of Hashimoto's thyroiditis in ultrasound using tissue characterization and pixel classification

Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine

Volumn 227, Issue 7, 2013, Pages 788-798

  Acharya, U R ^a,b   Sree, S Vinitha ^c   Mookiah, M R K ^a   Yantri, R ^d   Molinari, F ^e   Zieleznik W ^e   Malyszek Tumidajewicz J ^e   Stepien B ^e   Bardales, R H ^f   Witkowska, A ^g   Suri, J S ^c,h  

^a DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

^b UNIVERSITY OF MALAYA   (Malaysia)

^c Global Biomedical Technologies Inc   (United States)

^d POLITECNICO DI TORINO   (Italy)

^e Internal Medicine Practice   (Poland)

^f Outpatient Pathology Associates   (United States)

^g MEDICAL UNIVERSITY OF SILESIA   (Poland)

^h IDAHO STATE UNIVERSITY   (United States)

Author keywords

Computer aided diagnosis; Gabor wavelet; Hashimoto's thyroiditis; Higher order spectra; Ultrasonography

Indexed keywords

COMPUTER AIDED DIAGNOSTICS; CROSS-VALIDATION TECHNIQUE; GABOR WAVELETS; HASHIMOTO'S THYROIDITIS; HIGHER ORDER SPECTRUM; POSITIVE PREDICTIVE VALUES; RADIAL BASIS PROBABILISTIC NEURAL NETWORKS; SUPPORT VECTOR MACHINE CLASSIFIERS;

FORESTRY; SUPPORT VECTOR MACHINES; ULTRASONIC APPLICATIONS; ULTRASONICS; ULTRASONOGRAPHY;

COMPUTER AIDED DIAGNOSIS;

COMPUTER ASSISTED DIAGNOSIS; ECHOGRAPHY; FACTUAL DATABASE; HASHIMOTO DISEASE; HUMAN; PROCEDURES; REPRODUCIBILITY; SUPPORT VECTOR MACHINE; WAVELET ANALYSIS; ARTICLE; COMPUTER-AIDED DIAGNOSIS; GABOR WAVELET; HIGHER ORDER SPECTRA; METHODOLOGY;

DATABASES, FACTUAL; HASHIMOTO DISEASE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SUPPORT VECTOR MACHINES; WAVELET ANALYSIS;

COMPUTER-AIDED DIAGNOSIS; GABOR WAVELET; HASHIMOTO'S THYROIDITIS; HIGHER ORDER SPECTRA; ULTRASONOGRAPHY;

EID: 84884566354     PISSN: 09544119     EISSN: 20413033     Source Type: Journal    
DOI: 10.1177/0954411913483637     Document Type: Article

References (44)

1. Doǧan M, Acikgoz E, Acikgoz M, et al. The frequency of Hashimoto thyroiditis in children and the relationship between urinary iodine level and Hashimoto thyroiditis. J Pediatr Endocrinol Metab 2011; 24: 75-80.
2. Jaume JC. Endocrine autoimmunity. In: Gardner DG and Shoback DM (eds) Greenspan's basic and clinical endocrinology. New York: McGraw-Hill Medical, 2007, pp.59-79.
3. Helvaci MR, Seyhanli S and Cimbiz A. Ultrasonography should be accepted as a diagnostic criterion for autoimmune thyroiditis until demonstration of new antithyroidal antibodies. Res J Med Med Sci 2007; 2: 42-44.
4. Nordmeyer JP, Shafeh TA and Heckmann C. Thyroid sonography in autoimmune thyroiditis. A prospective study on 123 patients. Acta Endocrinol (Copenh) 1990; 122: 391-395.
5. Koprowski R and Wróbel Z. Analysis of properties of automatic analysis algorithm for areas in USG image in Hashimoto's disease. Adv Pattern Recogn Lect Notes in Comput Sci 2010; 6256: 98-106.
6. Müller HW, Schröder S, Schneider C, et al. Sonographic tissue characterisation in thyroid gland diagnosis. A correlation between sonography and histology. Klin Wochenschr 1985; 63: 706-710.
7. Schiemann U, Avenhaus W, Konturek JW, et al. Relationship of clinical features and laboratory parameters to thyroid echogenicity measured by standardized grey scale ultrasonography in patients with Hashimoto's thyroiditis. Med Sci Monit 2003; 9: MT13-MT17.
8. Marcocci C, Vitti P, Cetani F, et al. Thyroid ultrasonography helps to identify patients with diffuse lymphocytic thyroiditis who are prone to develop hypothyroidism. J Clin Endocrinol Metab 1991; 72: 209-213.
9. Pedersen OM, Aardal NP, Larssen TB, et al. The value of ultrasonography in predicting autoimmune thyroid disease. Thyroid 2000; 10: 251-259.
10. Loy M, Cianchetti ME, Cardia F, et al. Correlation of computerized gray-scale sonographic findings with thyroid function and thyroid autoimmune activity in patients with Hashimoto's thyroiditis. J Clin Ultrasound 2004; 32: 136-140.
11. Schiemann U, Gellner R, Riemann B, et al. Standardized grey scale ultrasonography in Graves' disease: correlation to autoimmune activity. Eur J Endocrinol 1999; 141: 332-336.
12. Mazziotti G, Sorvillo F, Iorio S, et al. Grey-scale analysis allows a quantitative evaluation of thyroid echogenicity in the patients with Hashimoto's thyroiditis. Clin Endocrinol (Oxf) 2003; 59: 223-229.
13. Schiemann U and Hengst K. Thyroid echogenicity in manic-depressive patients receiving lithium therapy. J Affect Disord 2002; 70: 85-90.
14. Schiemann U, Walli R, Schmidt F, et al. Thyroid hypoechogenicity in patients with chronic hepatitis receiving interferon-alpha therapy: evaluation by standardized grey scale ultrasonography. Eur J Med Res 2002; 7: 271-277.
15. Solivetti FM, Papini E, Misischi I, et al. "Hypoechoic Triangle": a new sonographic sign or marker of advanced autoimmune thyroiditis. Thyroid 2011; 21: 285-289.
16. Brown DE. Introduction to data mining for medical informatics. Clin Lab Med 2008; 28: 9-35.
17. Giannakakis GA, Tsiaparas NN, Xenikou MFS, et al. Wavelet entropy differentiations of event related potentials in dyslexia. In: 8th IEEE international conference on BioInformatics and BioEngineering (BIBE), 8-10 October, Athens, Greece, IEEE Publications, USA, 2008, pp.1-6.
18. Krishnan MMR, Acharya UR, Chakraborty C, et al. Automated diagnosis of oral cancer using higher order spectra features and local binary pattern: a comparative study. Technol Cancer Res Treat 2011; 10: 433-455.
19. Pharwaha APS and Singh B. Shannon and non-Shannon measures of entropy for statistical texture feature extraction in digitized mammograms. In: Proceedings of the world congress on engineering and computer science 2009 (WCECS 2009), vol. II, San Francisco, CA, 20-22 October, Newswood Limited, Hong Kong, 2009, pp. 1286-1291.
20. Manjunath BS and Ma WY. Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 1996; 18: 837-842.
21. Hu MK. Visual pattern recognition by moment invariants. IRE Trans Info Theory 1962; 8: 179-187.
22. Mandelbrot BB. The fractal geometry of nature. New York: WH Freeman, 1982.
23. Biswas MK, Ghose T, Guha S, et al. Fractal dimension estimation for texture images: a parallel approach. Pattern Recogn Lett 1998; 19: 309-313.
24. Chen EL, Chung PC, Chen CL, et al. An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Eng 1998; 45: 783-794.
25. Lundahl T, Ohley WJ, Kay SM, et al. Fractional Brownian motion: a maximum likelihood estimator and its application to image texture. IEEE Trans Med Imaging 1986; 5: 152-161.
26. Nikias C and Petropulu A. Higher-order spectral analysis. Englewood Cliffs, NJ: Prentice Hall, 1997.
27. Chua KC, Chandran V, Acharya UR, et al. Automatic identification of epileptic EEG signals using higher order spectra. Proc Inst Mech Eng H 2009; 223(4): 485-495.
28. Chua KC, Chandran V, Acharya UR, et al. Application of higher order spectra to identify epileptic EEG. J Med Syst 2011; 35(6): 563-571.
29. Ramm A and Katsevich A. The radon transform and local tomography. CRC Press, USA, 1996.
30. Bellman RE. Adaptive control processes: a guided tour. Princeton University Press, New Jersey, USA, 1961.
31. Box JF. Guinness, Gosset, Fisher, and small samples. Stat Sci 1987; 2: 45-52.
32. Burges J and Christopher C. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998; 2: 121-167.
33. Vapnik V. Statistical learning theory. 2nd ed. New York: Wiley, 1998.
34. Cortes C and Vapnik V. Support-vector networks. Mach Learn 1995; 20: 273-297.
35. David V and Sanchez A. Advanced support vector machines and kernel methods. Neurocomputing 2003; 55: 5-20.
36. Müller KR, Mika S, Rätsch G, et al. An introduction to kernel based learning algorithms. IEEE Trans Neural Netw 2001; 12: 181-201.
37. Specht DF. Probabilistic neural networks. Neural Netw 1990; 3: 109-118.
38. Larose DT. Discovering Knowledge in Data: An Introduction to Data Mining. John Wiley and Sons Inc, New Jersey, USA, 2004, pp.90-106.
39. Hsu CW, Chang CC and Lin CJ. A practical guide to support vector classification. Bioinformatics 2010; 1: 1-16.
40. Koprowski R, Wróbel Z and Zieleźnik W. Automatic ultrasound image analysis in Hashimoto's disease. In: MCPR'10: proceedings of the 2nd Mexican conference on pattern recognition: advances in pattern recognition. 27-29 September, Puebla, Mexico, Springer-Verlag, Berlin, Heidelberg, 2010, pp.98-106.
41. Mailloux G, Bertrand M, Stampfler R, et al. Computer analysis of echographic textures in Hashimoto disease of the thyroid. J Clin Ultrasound 1986; 14: 521-527.
42. Sara S, Smutek D, Sucharda P, et al. Systematic construction of texture features for Hashimoto's lymphocytic thyroiditis recognition from sonographic images. In: AIME'01: proceedings of the 8th conference on AI in medicine in Europe: artificial intelligence medicine. 1-4 July, Cascais, Portugal, Springer-Verlag, London, UK, 2001, pp.339-348.
43. Smutek D, Šára R, Sucharda P, et al. Image texture analysis of sonograms in chronic inflammations of thyroid gland. Ultrasound Med Biol 2003; 29: 1531-1543.
44. Anderson L, Middleton WD, Teefey SA, et al. Hashimoto thyroiditis: part 1, sonographic analysis of the nodular form of Hashimoto thyroiditis. Am J Roentgenol 2010; 195: 208-215.