Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound

IEEE Transactions on Medical Imaging

Volumn 31, Issue 10, 2012, Pages 1889-1899

  Gomez W ^a   Pereira, W C A ^b   Infantosi, A F C ^b  

^a DEPARTAMENTO DE FÍSICA   (Mexico)

^b FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

Breast ultrasound; co occurrence matrix; quantization level; tumor classification

Indexed keywords

AREA UNDER ROC CURVE (AUC); BENIGN LESION; BREAST LESION; BREAST ULTRASOUND; CLASSIFICATION PERFORMANCE; CLASSIFICATION PROCEDURE; CO-OCCURRENCE; CO-OCCURRENCE STATISTICS; CO-OCCURRENCE-MATRIX; FEATURE SPACE; FIGURE OF MERITS; FISHER LINEAR DISCRIMINANT ANALYSIS; GRAY LEVEL CO-OCCURRENCE MATRIX; GRAY LEVELS; GRAY SCALE; GRAY-LEVEL; MINIMUM BOUNDING RECTANGLE; MUTUAL INFORMATIONS; QUANTIZATION LEVELS; TEXTURE DESCRIPTORS; TEXTURE FEATURES; TUMOR CLASSIFICATION;

ITERATIVE METHODS; PIXELS; TEXTURES; ULTRASONICS;

ULTRASONIC APPLICATIONS;

ALGORITHM; AREA UNDER THE CURVE; ARTICLE; BREAST TUMOR; CLASSIFICATION; ECHOGRAPHY; ECHOMAMMOGRAPHY; FACTUAL DATABASE; FEMALE; HUMAN; IMAGE PROCESSING; METHODOLOGY; RECEIVER OPERATING CHARACTERISTIC;

ALGORITHMS; AREA UNDER CURVE; BREAST NEOPLASMS; DATABASES, FACTUAL; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; ROC CURVE; ULTRASONOGRAPHY, MAMMARY;

EID: 84867081344     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2012.2206398     Document Type: Article

References (63)

1. P. Skaane, "Ultrasonography as adjunct to mammography in the evaluation of breast tumors," Acta Radiologica Supplementum, vol. 420, pp. 1-47, 1999.
2. K. Kelly, J. Dean, S.-J. Lee, and W. Comulada, "Breast cancer detection: Radiologists' performance usingmammography with and without automated whole-breast ultrasound," Eur. Radiol., vol. 20, no. 11, pp. 2557-2564, 2010.
3. P. Crystal, S. D. Strano, S. Shcharynski, and M. J. Koretz, "Using sonography to screen women with mammographically dense breasts," AJR Am. J. Roentgenol., vol. 181, no. 1, pp. 177-182, 2003. (Pubitemid 36741481)
4. W. A. Berg et al., "Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer," Radiology, vol. 233, no. 3, pp. 830-849, 2004.
5. M. L. Giger, "Computer-aided diagnosis of breast lesions in medical images," Comput. Sci. Eng., vol. 2, no. 5, pp. 39-45, 2000.
6. A. T. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, "Solid breast nodules: Use of sonography to distinguish between benign and malignant lesions," Radiology, vol. 196, no. 1, pp. 123-134, 1995.
7. M. J. Calas, R.M. Almeida, B. Gutfilen, andW. C. A. Pereira, "Intraobserver interpretation of breast ultrasonography following the BI-RADS classification," Eur. Radiol., vol. 74, no. 3, pp. 525-528, 2010.
8. J. M. Thijssen, "Ultrasonic speckle formation, analysis and processing applied to tissue characterization," Pattern Recognit. Lett., vol. 24, no. 4, pp. 659-675, 2003. (Pubitemid 35326792)
9. M. Petrou and P. G. Sevilla, Image Processing. Dealing With Texture. Wiltshire, U.K.: Wiley, 2006.
10. R. G. Dantas, E. T. Costa, and S. Leeman, "Ultrasound speckle and equivalente scatterers," Ultrasonics, vol. 43, pp. 405-420, 2005.
11. A. V. Alvarenga, W. C. A. Pereira, A. F. C. Infantosi, and C. M. Azevedo, "Complexity curve and grey level co-occurrence matrix in the texture evaluation of breast tumor on ultrasound images," Med. Phys., vol. 34, no. 2, pp. 379-387, 2007. (Pubitemid 46185116)
12. B. S. Garra, B. H. Krasner, S. C. Horii, S. Ascher, S. K. Mun, and R. K. Zeman, "Improving the distinction between benign and malignant breast lesions: The value of sonographic texture analysis," Ultrasonic Imag., vol. 15, no. 4, pp. 267-285, 1993. (Pubitemid 24090199)
13. D.-R. Chen, R.-F. Chang, Y.-L. Huang, Y.-H. Chou, C.-M. Tiu, and P.-P. Tsai, "Texture analysis of breast tumors on sonograms," Seminars Ultrasound, CT, MRI, vol. 21, no. 4, pp. 308-316, 2000.
14. R.-F. Chang, W.-J. Wu, W. K. Moon, and D.-R. Chen, "Improvement in breast tumor discrimination by support vector machines and speckleemphasis texture analysis," Ultrasound Medicine Biol., vol. 29, no. 5, pp. 679-686, 2003. (Pubitemid 36569270)
15. Y.-L.Huang,K.-L.Wang, and D.-R. Chen, "Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines," Neural Comput. Applicat., vol. 15, no. 2, pp. 164-169, 2006. (Pubitemid 43190390)
16. D.-R. Chen, W.-J. Kuo, R.-F. Chang, W. K. Moon, and C. C. Lee, "Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound," Ultrasound Med. Biol., vol. 28, no. 7, pp. 897-902, 2002.
17. D.-R. Chen, R.-F. Chang, and Y.-L. Huang, "Breast cancer diagnosis using self-organizingmap for sonography," Ultrasound Med. Biol., vol. 26, no. 3, pp. 405-411, 2000. (Pubitemid 30204552)
18. K. Drukker, M. L. Giger, K. Horsch, M. A. Kupinski, C. J. Vyborny, and E. B. Mendelson, "Computerized lesion detection on breast ultrasound," Med. Phys., vol. 29, no. 7, pp. 1438-1446, 2002. (Pubitemid 34774575)
19. K. Horsch, M. L. Giger, L. A. Venta, and C. J. Vyborny, "Automatic segmentation of breast lesions on ultrasound," Med. Phys., vol. 28, no. 8, pp. 1652-1659, 2001. (Pubitemid 32783208)
20. K.Horsch,M. L. Giger, L. A.Venta, andC. J.Vyborny, "Computerized diagnosis of breast lesions on ultrasound," Med. Phys., vol. 29, no. 2, pp. 157-164, 2002.
21. K. Drukker, M. L. Giger, and E. B. Mendelson, "Computerized analysis of shadowing on breast ultrasound for improved lesion detection," Med. Phys., vol. 30, no. 7, pp. 1833-1842, 2003. (Pubitemid 36885802)
22. B. Sahiner et al., "Malignant and benign breast masses on 3D US volumetric images: Effect of computer-aided diagnosis on radiologist accuracy," Radiology, vol. 242, no. 3, pp. 716-724, 2007.
23. S. Joo, Y. S. Yang, W. K. Moon, and H. C. Kim, "Computer-aided diagnosis of solid breast nodules: Use of an artificial neural network based on multiple sonographic features," IEEE Trans.Med. Imag., vol. 23, no. 10, pp. 1292-1300, Oct. 2004.
24. D.-R. Chen, R.-F. Chang,W.-J. Kuo, M.-C. Chen, and Y. u.-L. Huang, "Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks," Ultrasound Med. Biol., vol. 28, no. 10, pp. 1301-1310, 2002. (Pubitemid 35418256)
25. P. M. Shankar et al., "Classification of breast masses in ultrasonic B scans using Nakagami and K distributions," Phys. Med. Biol., vol. 48, no. 14, pp. 2229-2240, 2003. (Pubitemid 36926710)
26. K. V. Mogatadakala, K. D. Donohue, C. W. Piccoli, and F. Forsberg, "Detection of breast lesion regions in ultrasound images using wavelets and order statistics," Med. Phys., vol. 33, no. 4, pp. 840-849, 2006.
27. D. R. Chen et al., "Classification of breast ultrasound images using fractal feature," Clin. Imag., vol. 29, no. 4, pp. 235-245, 2005.
28. Y. L. Huang, S. H. Lin, and D. R. Chen, "Computer-aided diagnosis applied to 3-D US of solid breast nodules by using principal component analysis and image retrieval," in Conf. Proc. IEEE Eng. Med. Biol. Soc., 2005, vol. 2, pp. 1802-1805.
29. S.-J. Chen et al., "The representation of sonographic image texture for breast cancer using co-occurrence matrix," J. Med. Biol. Eng., vol. 25, no. 4, pp. 1993-1999, 2005.
30. S. Huber, J. Danes, I. Zuna, J. Teubner, M. Medl, and S. Delorme, "Relevance of sonographic B-mode criteria and computer-aided ultrasonic tissue characterization in differential/diagnosis of solid breast masses," Ultrasound Med. Biol., vol. 26, no. 8, pp. 1243-1252, 2000.
31. F. Lefebvre,M.Meunier, F. Thibault, P. Laugier, andG. Berger, "Computerized ultrasound B-scan characterization of breast nodules," Ultrasound Med. Biol., vol. 26, no. 9, pp. 1421-1428, 2000. (Pubitemid 32118211)
32. R. Sivaramakrishna, K. A. Powell, M. L. Lieber, W. A. Chilcote, and R. Shekhar, "Texture analysis of lesions in breast ultrasound images," Computerized Med. Imag. Graphics, vol. 26, no. 5, pp. 303-307, 2002. (Pubitemid 36151585)
33. W. J. Kuo, R. F. Chang, C. C. Lee, W. K. Moon, and D. R. Chen, "Retrieval technique for the diagnosis of solid breast tumors on sonogram," Ultrasound Med. Biol., vol. 28, no. 7, pp. 903-909, 2002.
34. W.Gómez, L. Leija, A. V. Alvarenga, A. F. C. Infantosi, andW. C.A. Pereira, "Computerized lesion segmentation of breast ultrasound based on marker-controlled watershed transformation," Med. Phys., vol. 37, no. 1, pp. 82-95, 2010.
35. L. K. Soh and C. Tsatsoulis, "Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 780-795, Feb. 1999.
36. R. M. Haralick, K. Shanmugam, and I. H. Dinstein, "Textural features for image classification," IEEE Trans. Syst., Man Cybern., vol. 3, no. 6, pp. 610-621, Jun. 1973.
37. B. Liu, H. D. Cheng, J. Huang, J. Tian, J. Liu, and X. Tang, "Automated segmentation of ultrasonic breast lesions using statistical texture classification and active contour based on probability distance," Ultrasound Med. Biol., vol. 35, no. 8, pp. 1309-1324, 2009.
38. Y. Su, Y. Wang, J. Jiao, and Y. Guo, "Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features," Open Med. Informat. J., vol. 5, no. Suppl 1, pp. 26-37, 2011.
39. S.-J. Chen et al., "The representations of sonographic image texture for breast cancer using co-occurrence matrix," J. Med. Biol. Eng., vol. 25, no. 4, pp. 193-199, 2005. (Pubitemid 43075669)
40. C. Qiuxia and L. Qi, "Textural feature analysis for ultrasound breast tumor images," in Proc. 4th Int. Conf. Bioinformat. Biomed. Eng. (iCBBE 2010), 2010, pp. 1-4.
41. B. Liu, H. D. Cheng, J. Huang, J. Tian, X. Tang, and J. Liu, "Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images," Pattern Recognit., vol. 43, pp. 280-298, 2010.
42. , Natick, Ed., Matlab R2011a. Image Processing Toolbox. Natick, MA: MathWorks, 2011.
43. M. Bevk and I. Kononenko, "A statistical approach to texture description ofmedical images:Apreliminary study," in Proc. 15th IEEE Symp. Computer-Based Med.Syst. (CBMS 2002), 2002, pp. 239-244. (Pubitemid 34773178)
44. D. A. Clausi, "An analysis of co-occurrence texture statistics as a function of grey level quantization," Canad. J. Remote Sens., vol. 28, no. 1, pp. 45-62, 2002.
45. C.-J. Tu, L.-Y. Chuang, J.-Y. Chang, and C.-H. Yang, "Feature selection using PSO-SVM," Int. J. Comput. Sci., vol. 33, no. 1, pp. 138-143, 2006.
46. H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information: Criteria of max-dependency,max-relevance, and min-redundancy," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226-1238, Aug. 2005. (Pubitemid 41245053)
47. G.D.Tourassi, E. D. Frederick,M. K.Markey, andC. E. Floyd, "Application of the mutual information criterion for feature selection in computer-aided diagnosis," Med. Phys., vol. 28, no. 12, pp. 2394-2402, 2001. (Pubitemid 34031524)
48. W. Gómez, L. Leija, and A. Díaz-Pérez, "Mutual information and intrinsic dimensionality for feature selection," in Proc. 7th Int. Conf. Elect. Eng., Comput. Sci. Automatic Control, Tuxtla Gutiérrez,Mexico, 2010, pp. 339-344.
49. W. C. Pereira, A. V. Alvarenga, A. F. Infantosi, L. Macrini, and C. E. Pedreira, "A non-linear morphometric feature selection approach for breast tumor contour fromultrasonic images," Comput. Biol.Med., vol. 40, no. 11-12, pp. 912-918, 2010.
50. J. Dougherty, R. Kohavi, and M. Sahami, "Supervised and unsupervised discretization of continuous features," in Proc. 12th Int. Conf. Mach. Learn., 1995, pp. 102-194.
51. A. V. Alvarenga, A. F. Infantosi, W. C. Pereira, and C. M. Azevedo, "Assessing the performance of morphological parameters in distinguishing breast tumors on ultrasound images," Med. Eng. Phys., vol. 32, pp. 49-56, 2009.
52. J. L. Jesneck, J. Y. Lo, and J. A. Baker, "Breast mass lesions: Computer-aided diagnosis models with mammographic and sonographic descriptors," Radiology, vol. 244, no. 2, pp. 390-398, 2007. (Pubitemid 47080870)
53. K. Drukker, L. Pesce, and M. Giger, "Repeatability in computer-aided diagnosis:Application to breast cancer diagnosis on sonography," Med. Phys., vol. 37, no. 6, pp. 2659-2669, 2010.
54. P. A. Lachenbruch and M. Goldstein, "Discriminant analysis," Biometrics, vol. 35, no. 1, pp. 69-85, 1979. (Pubitemid 9136567)
55. P. A. Lachenbruch, "An almost unbiased method of obtaining confidence intervals for the probability of misclassification in discriminant analysis," Biometrics, vol. 23, no. 4, pp. 639-645, 1967.
56. B. Efron and R. Tibshirani, "Improvements on cross-validation: The method," J. Amer. Stat. Assoc., vol. 92, no. 438, pp. 548-560, 1997.
57. B. Sahiner, H.-P. Chan, and L. Hadjiiski, "Classifier performance prediction for computer-aided diagnosis using a limited dataset," Med. Phys., vol. 35, no. 4, pp. 1559-1570, 2008. (Pubitemid 351457523)
58. W. Jiang and R. Simon, "A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification," Stat. Med., vol. 26, no. 29, pp. 5320-5334, 2007. (Pubitemid 350285115)
59. K. L. Priddy and P. E. Keller, Artificial Neural Networks: An Introduction. Bellingham, WA: SPIE-The Int. Soc. Optical Eng., 2005.
60. S. Ulrich, P. Christian, S. Stefan, H. Christian, and E. Helmut, "A tutorial on the use of ROC analysis for computer-aided diagnostic systems," Ultrason. Imag., vol. 27, no. 3, pp. 181-198, 2005.
61. T. Fawcett, "An introduction to ROC analysis," Pattern Recognit. Lett., vol. 27, no. 8, pp. 861-874, 2006.
62. C. E. Metz, B. A. Herman, and C. A. Roe, "Statistical comparison of two ROC-curve estimates obtained from partially-paired datasets," Med. Decision Making, vol. 18, no. 1, pp. 110-121, 1998. (Pubitemid 28041647)
63. M. R. Stoline, "The status of multiple comparisons: Simultaneous estimation of all pairwise comparisons in one-way ANOVA designs," Amer. Stat., vol. 3, no. 3, pp. 134-141, 1981.