Non-invasive automated 3D thyroid lesion classification in ultrasound: A class of ThyroScan™ systems

Ultrasonics

Volumn 52, Issue 4, 2012, Pages 508-520

  Rajendra Acharya, U ^a   Vinitha Sree, S ^b   Muthu Rama Krishnan, M ^a   Molinari, Filippo ^c   Garberoglio, Roberto ^d   Suri, Jasjit S ^b,e  

^a DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

^b Global Biomedical Technologies Inc   (United States)

^c POLITECNICO DI TORINO   (Italy)

^d Scientific Foundation Mauriziana Onlus   (Italy)

^e IDAHO STATE UNIVERSITY   (United States)

Author keywords

Computer Aided Diagnosis; Contrast Enhanced Ultrasound; High Resolution Ultrasound; Texture; Thyroid lesion

Indexed keywords

CLASSIFICATION ACCURACY; COMPUTER AIDED DIAGNOSTICS; CONTRAST ENHANCED ULTRASOUND; DATA SETS; DESCRIPTORS; FEATURE VECTORS; FINE NEEDLE ASPIRATION BIOPSIES; FOURIER SPECTRA; FUZZY CLASSIFIERS; GAUSSIAN MIXTURE MODEL; HIGH RESOLUTION ULTRASOUND; K-NEAREST NEIGHBORS; LESION CLASSIFICATION; LOCAL BINARY PATTERNS; NAIVE BAYES CLASSIFIERS; NON-INVASIVE; OFF-LINE TRAINING; ON-LINE TESTS; RADIAL BASIS PROBABILISTIC NEURAL NETWORKS; TEXTURE CHARACTERISTICS; TEXTURE ENERGY; TEXTURE FEATURES; THYROID LESION; THYROID NODULE; ULTRASOUND IMAGES;

BIOPSY; CLASSIFIERS; COMPUTER AIDED DIAGNOSIS; DECISION TREES; FINITE DIFFERENCE METHOD; FRACTAL DIMENSION; IMAGE RETRIEVAL; MAMMOGRAPHY; NEURAL NETWORKS; SUPPORT VECTOR MACHINES; TEXTURES; ULTRASONIC APPLICATIONS; ULTRASONICS;

THREE DIMENSIONAL;

CONTRAST AGENT BR1; CONTRAST MEDIUM; DIAGNOSTIC AGENT; PHOSPHOLIPID; SULFUR HEXAFLUORIDE;

ADULT; AGED; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CLASSIFICATION; COMPUTER ASSISTED DIAGNOSIS; DECISION TREE; ECHOGRAPHY; FEMALE; FOURIER ANALYSIS; FRACTAL ANALYSIS; FUZZY LOGIC; HUMAN; MALE; MIDDLE AGED; NONPARAMETRIC TEST; PROBABILITY; SUPPORT VECTOR MACHINE; THREE DIMENSIONAL IMAGING; THYROID NODULE;

ADULT; AGED; ALGORITHMS; BAYES THEOREM; CONTRAST MEDIA; DECISION TREES; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; FOURIER ANALYSIS; FRACTALS; FUZZY LOGIC; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PHOSPHOLIPIDS; PROBABILITY; STATISTICS, NONPARAMETRIC; SULFUR HEXAFLUORIDE; SUPPORT VECTOR MACHINES; THYROID NODULE;

EID: 84856515484     PISSN: 0041624X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ultras.2011.11.003     Document Type: Article

References (58)

1. S. Feld AACE clinical practice guidelines for the diagnosis and management of thyroid nodules Endocr. Pract. 1996 78 84
2. NCI (National Cancer Institute) on thyroid cancer, < http://www.cancer.gov/cancertopics/types/thyroid >.
3. ACS (American Cancer Society), What are the key statistics about thyroid cancer? < http://www.cancer.org/Cancer/ThyroidCancer/DetailedGuide/thyroid- cancer-key-statistics >.
4. US CSWG (US Cancer Statistics Working Group), United States Cancer Statistics: 2000 Incidence, Atlanta, GA: Department of Health and Human Services, Centre for Disease Control and Prevention and National Cancer Institute, 2003.
5. S.A. Polyzos, M. Kita, and A. Avramidis Thyroid nodules - stepwise diagnosis and management Hormones 6 2007 101 119
6. C.D. Lansford, and T.N. Teknos Evaluation of the thyroid nodule Cancer Control 13 2006 89 98
7. Z.W. Baloch, S. Fleisher, V.A. LiVolsi, and P.K. Gupta Diagnosis of follicular neoplasm: a gray zone in thyroid fine-needle aspiration cytology Diagn. Cytopathol. 26 2002 1 44
8. M.R. Castro, and H. Gharib Thyroid fine-needle aspiration biopsy: progress, practice, and pitfalls Endocr. Pract. 9 2003 128 136
9. G.D. Soto, I. Halperin, M. Squarcia, F. Lomeña, and M.P. Domingo Update in thyroid imaging. The expanding world of thyroid imaging and its translation to clinical practice Hormones 9 2010 287 298
10. D.S. Cooper, G.M. Doherty, and B.R. Haugen Revised American thyroid association management guidelines for patients with thyroid nodules and differentiated thyroid cancer Thyroid 19 2009 1167 1214
11. F. Pacini, M. Schlumberger, and H. Dralle European thyroid cancer taskforce, European consensus for the management of patients with differentiated thyroid carcinoma of the follicular epithelium Eur. J. Endocrinol. 154 2006 787 803 (Pubitemid 43997716)
12. S.J. Mandel Diagnostic use of ultrasonography in patients with nodular thyroid disease Endocr. Pract. 10 2004 246 252
13. S. Bastin, M.J. Bolland, and M.S. Croxson Role of ultrasound in the assessment of nodular thyroid disease J. Med. Imag. Radiat. Oncol. 53 2009 177 187
14. D.E. Brown Introduction to data mining for medical informatics Clin. Lab. Med. 28 2008 9 35
15. G. Ivanac, B. Brkljacic, K. Ivanac, R. Huzjan, F. Skreb, and I. Cikara Vascularisation of benign and malignant thyroid nodules: CD US evaluation Ultraschall Med. 28 2007 502 506 (Pubitemid 350033782)
16. Z.V. Maizlin, S.M. Wiseman, P. Vora, J.M. Kirby, A.C. Mason, D. Filipenko, and J.A. Brown Hurthle cell neoplasms of the thyroid: sonographic appearance and histologic characteristics J. Ultrasound Med. 27 2008 751 757
17. A. Lyshchik, R. Moses, S.L. Barnes, T. Higashi, R. Asato, M.I. Miga, J.C. Gore, and A.C. Fleischer Quantitative analysis of tumor vascularity in benign and malignant solid thyroid nodules J. Ultrasound Med. 26 2007 837 846 (Pubitemid 46870352)
18. M.C. Frates, C.B. Benson, P.M. Doubilet, E.S. Cibas, and E. Marqusee Can color Doppler sonography aid in the prediction of malignancy of thyroid nodules? J. Ultrasound Med. 22 2003 127 131 (Pubitemid 36143084)
19. T. Rago, F. Santini, M. Scutari, A. Pinchera, and P. Vitti Elastography: new developments in ultrasound for predicting malignancy in thyroid nodules J. Clin. Endocr. Metab. 92 2007 2917 2922 (Pubitemid 47236350)
20. T.V. Bartolotta, M. Midiri, M. Galia, G. Runza, M. Attard, G. Savoia, R. Lagalla, and A.E. Cardinale Qualitative and quantitative evaluation of solitary thyroid nodules with contrast-enhanced ultrasound: initial results Eur. Radiol. 16 2006 2234 2241 (Pubitemid 44349872)
21. M. Appetecchia, D. Bacaro, R. Brigida, D. Milardi, A. Bianchi, and F. Solivetti Second generation ultrasonographic contrast agents in the diagnosis of neoplastic thyroid nodules J. Exp. Clin. Cancer Res. 25 2006 325 330 (Pubitemid 44763723)
22. B. Zhang, Y.X. Jiang, J.B. Liu, M. Yang, Q. Dai, Q.L. Zhu, and P. Gao Utility of contrast-enhanced ultrasound for evaluation of thyroid nodules Thyroid 20 2010 51 57
23. M. Schneider, M. Arditi, and M.B. Barrau BRI: a new ultrasonographic contrast agent based on sulphur hexafluoride-filled microbubbes Invest. Radiol. 30 2005 451 457
24. F. Molinari, A. Mantovani, M. Deandrea, P. Limone, R. Garberoglio, and J.S. Suri Characterization of single thyroid nodules by contrast-enhanced 3-D ultrasound Ultrasound Med. Biol. 36 2010 1616 1625
25. R. Mihai, A.J. Parker, D. Roskell, and G.P. Sadler One in four patients with follicular thyroid cytology (THY3) has a thyroid carcinoma Thyroid 19 2009 33 37
26. K.T. Wong, and A.T. Ahuja Ultrasound of thyroid cancer Cancer Imaging 5 2005 157 166
27. C. Fortin Fractal dimension in the analysis of medical images IEEE Eng. Med. Biol. 11 1992 65 71
28. B.B. Chaudhuri, and N. Sarkar Texture segmentation using fractal dimension IEEE Trans. Pattern Anal. Mach. Intell. 17 1995 72 77
29. M.K. Biswas, T. Ghose, S. Guha, and P.K. Biswas Fractal dimension estimation for texture images: a parallel approach Pattern Recogn. Lett. 19 1998 309 313 (Pubitemid 128383129)
30. T. Ojala, M. Pietikäinen, and D. Harwood A comparative study of texture measures with classification based on feature distributions Pattern Recogn. 29 1996 51 59
31. T. Ojala, M. Pietikäinen, and T. Maenpaa Multiresolution gray-scale and rotation invariant texture classification with local binary patterns IEEE Trans. Pattern Anal. 24 2002 971 987 (Pubitemid 34835471)
32. S. Liao, M.W.K. Law, and A.C.S. Chung Dominant Local Binary Patterns for Texture Classification IEEE Trans. Image Process. 18 2009 1107 1118
33. B. Zhang, Y. Gao, S. Zhao, and J. Liu Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor IEEE Trans. Image Process. 19 2010 533 544
34. R.C. Gonzalez, and R.E. Woods Digital Image Processing second ed. 2002 Prentice Hall New York pp. 655-659
35. R. Gupta, and P.E. Undrill The use of texture analysis to delineate suspicious masses in mammography Phys. Med. Biol. 40 1995 835 855
36. M. Petrou, and P.G. Sevilla Image Processing - Dealing with Texture 2006 John Wiley & Sons pp. 297-606
37. K.I. Laws Rapid texture identification SPIE Conf. Ser. 238 1980 376 380
38. C. Chappard, B.B. Imbault, and G. Lemineur Anisotropy changes in post-menopausal osteoporosis: characterization by a new index applied to trabecular bone radiographic images Osteoporosis Int. 16 2005 1193 1202 (Pubitemid 41396946)
39. C.L. Benhamou Texture analysis on bone radiographs, bone quality seminars Osteoporosis Int. 18 2007 864 867
40. W.J. Conover Practical Nonparametric Statistics third ed. 1999 John Wiley & Sons pp. 271-286
41. C. Cortes, and V. Vapnik Support-vector networks Mach. Learn. 20 1995 273 297
42. V. David, and A. Sanchez Advanced support vector machines and kernel methods Neurocomputing 55 2003 5 20
43. K.R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf An introduction to kernel based learning algorithms IEEE Trans. Neural Networks 12 2001 181 201 (Pubitemid 32371478)
44. J.A. Bilmes A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and Hidden Markov models Int. Comput. Sci. Inst. 1998 1 13
45. D.F. Specht Probabilistic neural networks J. Neural Networks 3 1990 109 118
46. D.T. Larose Discovering Knowledge in Data: An Introduction to Data Mining 2004 Wiley Interscience New Jersey, USA (Chapter 5: KNN)
47. J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2005, pp. 291-350 (310-311).
48. D.T. Larose Discovering Knowledge in Data: An Introduction to Data Mining 2004 Wiley Interscience New Jersey, USA pp. 108-126 (Chapter 6: Decision Trees)
49. T.J. Ross Fuzzy Logic with Engineering Applications 2004 John Wiley & Sons Ltd. West Sussex, UK pp. 148-162
50. M. Sugeno Industrial Applications of Fuzzy Control 1985 Elsevier Science Pub. Co. 0444878297 p. 278
51. C.W. Hsu, C.C. Chang, and C.J. Lin A practical guide to support vector classification Bioinformatics 1 2010 1 16
52. M.M. D'Souza, R.K. Marwaha, R. Sharma, A. Jaimini, S. Thomas, D. Singh, M. Jain, P.J. Bhalla, M. Tripathi, A. Tiwari, A. Mishra, A. Mondal, and R.P. Tripathi Prospective evaluation of solitary thyroid nodule on 18F-FDG PET/CT and high-resolution ultrasonography Ann. Nucl. Med. 24 2010 345 355
53. D.J. Finley, B. Zhu, C.B. Barden, and T.J. Fahey Discrimination of benign and malignant thyroid nodules by molecular profiling Ann. Surg. 240 2004 425 437 (Pubitemid 39121337)
54. J.M. Cerutti, R. Delcelo, M.J. Amadei, C. Nakabashi, R.M.B. Maciel, B. Peterson, J. Shoemaker, and G.J. Riggins A preoperative diagnostic test that distinguishes benign from malignant thyroid carcinoma based on gene expression J. Clin. Invest. 113 2004 1234 1242 (Pubitemid 38544109)
55. J.A. Patton, J.W. Hollifield, A.B. Brill, G.S. Lee, and D.D. Patton Differentiation between malignant and benign solitary thyroid nodules by fluorescent scanning J. Nucl. Med. 17 1976 17 21
56. J. Ding, H. Cheng, C. Ning, J. Huang, and Y. Zhang Quantitative measurement for thyroid cancer characterization based on elastography J. Ultrasound Med. 30 2011 1259 1266
57. U.R. Acharya, O. Faust, S. Vinitha Sree, F. Molinari, R. Garberoglio, and J.S. Suri Cost-effective and non-invasive automated benign & 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 2011 295 390
58. S.J. Chen, S.N. Yu, J.E. Tzeng, Y.T. Chen, K.Y. Chang, K.S. Cheng, F.T. Hsiao, and C.K. Wei Characterization of the major histopathological components of thyroid nodules using sonographic textural features for clinical diagnosis and management Ultrasound Med. Biol. 35 2009 201 208