Assessing the combined performance of texture and morphological parameters in distinguishing breast tumors in ultrasound images

Medical Physics

Volumn 39, Issue 12, 2012, Pages 7350-7358

  Alvarenga, André Victor ^a   Infantosi, Antonio Fernando C ^b   Pereira, Wagner C A ^b   Azevedo, Carolina M ^c  

^a INSTITUTO NACIONAL DE METROLOGIA   (Brazil)

^b FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

^c FEDERAL UNIVERSITY OF THE STATE OF RIO DE JANEIRO   (Brazil)

Author keywords

Breast cancer; image processing; morphological parameters; texture parameters; ultrasound images

Indexed keywords

DIAGNOSIS; DISCRIMINANT ANALYSIS; IMAGE PROCESSING; MEDICAL IMAGING; TEXTURES; TUMORS; ULTRASONIC APPLICATIONS;

BREAST CANCER; LINEAR DISCRIMINANT ANALYSIS; MORPHOLOGICAL PARAMETERS; NEGATIVE PREDICTIVE VALUE; POSITIVE PREDICTIVE VALUES; RECEIVER OPERATOR CHARACTERISTIC CURVES; TEXTURE PARAMETERS; ULTRASOUND IMAGES;

IMAGE TEXTURE;

EID: 84870931846     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.4766268     Document Type: Article

References (50)

1. N. Houssami, L. Irwig, and C. Loy, Accuracy of combined breast imaging in young women., The Breast 11, 36-40 (2002). 10.1054/brst.2001.0355
2. T. Kolb, J. Lichy, and J. Newhouse, Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: An analysis of 27,825 patient evaluations., Radiology 225, 165-175 (2002). 10.1148/radiol.2251011667
3. S. Kaplan, Clinical utility of bilateral whole-breast US in the evaluation of women with dense breast tissue., Radiology 221, 641-649 (2001). 10.1148/radiol.2213010364
4. P. Skaane, Ultrasonography as adjunct to mammography in the evaluation of breast tumors., Acta Radiol., Suppl. 40, 1-47 (1999).
5. G. Rahbar, A. C. Sie, G. C. Hansen, J. S. Prince, M. L. Melany, H. E. Reynolds, V. P. Jackson, J. W. Sayre, and L. W. Basset, Benign versus malignant solid breast masses: US differentiation., Radiology 213, 889-894 (1999).
6. L. Duijm, Sensitivity, specificity and predictive values of breast imaging in the detection of cancer., Br. J. Cancer 76, 377-381 (1997). 10.1038/bjc.1997.393
7. A. Stavros, Solid breast nodules: Use of sonography to distinguish between benign and malignant lesions., Radiology 196, 123-134 (1995).
8. 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. 26, 1243-1252 (2000). 10.1016/S0301-5629(00)00274-X
9. Y. Chou, C. Tiu, G. Hung, S. C. Wu, T. Y. Chang, and H. K. Chiang, Stepwise logistic regression analysis of tumour contour features for breast ultrasound diagnosis., Ultrasound Med. Biol. 27, 1493-1498 (2001). 10.1016/S0301-5629(01)00466-5
10. S. Y. Chiou, Y. H. Chou, H. J. Chiou, H. K. Wang, C. M. Tiu, L. M. Tseng, and C. Y. Chang, Sonographic features of nonpalpable breast cancer: A study based on ultrasound-guided wire-localized surgical biopsies., Ultrasound Med. Biol. 32, 1299-1306 (2006). 10.1016/j.ultrasmedbio.2006.05.018
11. K. Horsch, M. L. Giger, L. A. Venta, and C. J. Vyborny, Computerized diagnostic of breast lesions on ultrasound., Med. Phys. 29, 157-164 (2002). 10.1118/1.1429239
12. M. L. Giger, H. AI-Hallaq, Z. Huo, C. Moran, D. E. Woiverton, C. W. Chan, and W. Zhong, Computerized analysis of lesions in US images of the breast., Acad. Radiol. 6, 665-674 (1999). 10.1016/S1076-6332(99)80115-9
13. K. G. Kim, S. W. Cho, S. J. Min, J. H. Kim, B. G. Min, and K. T. Bae, Computerized scheme for assessing ultrasonographic features of breast masses., Acad. Radiol. 12, 58-66 (2005). 10.1016/j.acra.2004.11.010
14. C. M. Chen, Y. H. Chou, K. C. Han, G. S. Hung, C. M. Tiu, H. J. Chiou, and S. Y. Chiou, Breast lesions on sonograms: Computer-aided diagnosis with nearly setting-independent features and artificial neural networks., Radiology 226, 504-514 (2003). 10.1148/radiol.2262011843
15. 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. 29, 1438-1446 (2002). 10.1118/1.1485995
16. R. Chang, Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors., Breast Cancer Res. Treat. 89, 179-185 (2005). 10.1007/s10549-004-2043-z
17. J. Kilday, F. Palmieri, and M. D. Fox, Classifying mammographic lesions using computerized image analysis., IEEE Trans. Med. Imaging 12, 664-669 (1993). 10.1109/42.251116
18. A. V. Alvarenga, A. F. C. Infantosi, W. C. A. Pereira, and C. M. Azevedo, Assessing the performance of the normalised radial length and convex polygons in distinguishing breast tumours on ultrasound images., Braz. J. Biomed. Eng. 22, 181-189 (2006).
19. W. J. Wu and W. K. Moon, Ultrasound breast tumor image computer-aided diagnosis with texture and morphological features., Acad. Radiol. 15, 873-880 (2008). 10.1016/j.acra.2008.01.010
20. R. M. Rangayyan and T. M. Nguyen, Fractal analysis of contours of breast masses in mammograms., J. Digit Imaging 20, 223-237 (2007). 10.1007/s10278-006- 0860-9
21. D. Guliato, J. D. de Carvalho, R. M. Rangayyan, and S. A. Santiago, Feature extraction from a signature based on the turning angle function for the classification of breast tumors., J. Digit Imaging 21, 129-144 (2008). 10.1007/s10278-007-9069-9
22. D. R. Chen, R. F. Chang, W. J. Kuo, M. C. Chen, and Y. L. Huang, Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks., Ultrasound Med. Biol. 28, 1301-1310 (2002). 10.1016/S0301-5629(02)00620-8
23. V. Goldberg, A. Manduca, D. L. Ewert, J. J. Gisvold, and J. F. Greenleaf, Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence., Med. Phys. 19, 1475-1481 (1992). 10.1118/1.596804
24. 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., Ultrason. Imaging 15, 267-285 (1993). 10.1006/uimg.1993.1017
25. F. Lefebvre, M. Meunier, F. Thibault, P. Laugier, and G. Berger, Computerized ultrasound B-scan characterization of breast nodules., Ultrasound Med. Biol. 26, 1421-1428 (2000). 10.1016/S0301-5629(00)00302-1
26. R. Sivaramakrishna, K. A. Powell, M. L. Lieber, W. A. Chilcote, and R. Shekhar, Texture analysis of lesions in breast ultrasound images., Comput. Med. Imaging Graph. 26, 303-307 (2002). 10.1016/S0895-6111(02)00027-7
27. W. J. Kuo, R. F. Chang, and C. C. Lee, Retrieval technique for the diagnosis of solid breast tumors on sonogram., Ultrasound Med. Biol. 28, 903-909 (2002). 10.1016/S0301-5629(02)00541-0
28. D. Chen, R. Chang, and Y. Huang, Breast cancer diagnosis using self-organizing map for sonography., Ultrasound Med. Biol. 26, 405-411 (2000). 10.1016/S0301-5629(99)00156-8
29. R. F. Chang, W. J. Wu, W. K. Moon, and D. R. Chen, Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis., Ultrasound Med. Biol. 29, 679-686 (2003). 10.1016/S0301-5629(02) 00788-3
30. A. V. Alvarenga, A. F. C. Infantosi, W. C. A. Pereira, and C. M. Azevedo, Assessing texture parameters to quantify breast tumour echotexture in ultrasound images., in IFMBE Proceedings of the 10th Mediterranean Conference on Medical and Biological Engineering (MEDICON, Naples, Italy, 2004), pp. 1-4.
31. 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. 34, 379-387 (2007). 10.1118/1.2401039
32. A. V. Alvarenga, A. F. C. Infantosi, C. M. Azevedo, and W. C. A. Pereira, Assessing the performance of morphological parameters in distinguishing breast tumors on ultrasound images., Med. Eng. Phys. 32, 49-56 (2010). 10.1016/j.medengphy.2009.10.007
33. Y.-L. Huang, D.-R. Chen, Y.-R. Jiang, S.-J. Kuo, H.-K. Wu, and W. K. Moon, Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound., Ultrasound Obstet. Gynecol. 32, 565-572 (2008). 10.1002/uog.5205
34. W.-C. Shen, R.-F. Chang, W. K. Moon, Y.-H. Chou, and C.-S. Huang, Breast ultrasound computer-aided diagnosis using BI-RADS features., Acad. Radiol. 14, 928-939 (2007). 10.1016/j.acra.2007.04.016
35. J. Cui, B. Sahiner, H.-P. Chan, A. Nees, C. Paramagul, L. M. Hadjiiski, C. Zhou, and J. Shi, A new automated method for the segmentation and characterization of breast masses on ultrasound images., Med. Phys. 36, 1553-1565 (2009). 10.1118/1.3110069
36. Y.-Y. Liao, P.-H. Tsui, C.-H. Li, K.-J. Chang, W.-H. Kuo, C.-C. Chang, and C.-K. Yeh, Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images., Med. Phys. 38, 2198-2207 (2011). 10.1118/1.3566064
37. R. M. Haralick, K. Shanmugam, and I. Dinstein, Textural features for image classification.. IEEE Trans. Syst. Man Cybern. 3, 610-621 (1973). 10.1109/TSMC.1973.4309314
38. S. Baheerathan, F. Albregtsen, and H. E. Danielsen, New texture features based on the complexity curve., Pattern Recogn. 32, 605-618 (1999). 10.1016/S0031-3203(98)00122-8
39. P. Soille, Morphological Image Analysis (Springer-Verlag, Heidelberg, 1999).
40. K. D. Donohue, L. Huang, T. Burks, F. Forsberg, and C. W. Piccoli, Tissue classification with generalized spectrum parameters., Ultrasound Med. Biol. 27, 1505-1514 (2001). 10.1016/S0301-5629(01)00468-9
41. S. Dutta, B. J. Barber, and S. Parameswaran, Texture analysis of protein distribution images to find differences due to aging and superfusion., Ann. Biomed. Eng. 23, 772-786 (1995). 10.1007/BF02584476
42. T. Hill and P. Lewicki, Electronic Statistics Textbook: Discriminante Function Analysis (2008) (available URL: http://www.statsoft.com/textbook/ stdiscan.html).
43. General Practice Notebook, A UK medical encyclopaedia on the world wide web (2008) (available URL: http://www.gpnotebook.co.uk/).
44. C. E. Metz, ROC methodology in radiologic imaging., Invest. Radiol. 21, 720-733 (1986). 10.1097/00004424-198609000-00009
45. S. H. Park, J. M. Goo, and C. H. Jo, Receiver operating characteristic (ROC) curve: Practical review for radiologists., Korean J. Radiol. 5, 11-18 (2004). 10.3348/kjr.2004.5.1.11
46. T. Hill, P. Lewicki, Electronic Statistics Textbook: Basic Statistics (2008) (available URL: http://www.statsoft.com/textbook/stbasic.html).
47. C. M. Azevedo, A. V. Alvarenga, W. C. A. Pereira, and A. F. C. Infantosi, Computer-assisted analysis of breast tumors texture on sonographic images of patients submitted to breast conserving surgery., Radiol. Bras. 42, 363-369 (2009). 10.1590/S0100-39842009000600009
48. N. Masciadri and C. Ferranti, Benign breast lesions: Ultrasound., J. Ultrasound 14, 55-65 (2011). 10.1016/j.jus.2011.03.002
49. M. J. G. Calas, A. V. Alvarenga, B. Gutfilen, and W. C. A. Pereira, Evaluation of morphometric parameters calculated from breast lesion contours at ultrasonography in the distinction among BI-RADS categories., Radiol. Bras. 44, 289-296 (2011). 10.1590/S0100-39842011000500006
50. See supplementary material at http://dx.doi.org/10.1118/1.4766268 E-MPHYA6-39-021212 for the Appendix.