Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis

British Journal of Radiology

Volumn 80, Issue 956, 2007, Pages 648-656

  Karahaliou, A ^a   Skiadopoulos, S ^a   Boniatis, I ^a   Sakellaropoulos, P ^a   Likaki, E ^a   Panayiotakis, G ^a   Costaridou, Lena ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BREAST BIOPSY; BREAST CANCER; BREAST TUMOR; CALCIFICATION; CANCER DIAGNOSIS; COMPUTER ASSISTED DIAGNOSIS; CONTRAST ENHANCEMENT; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; HUMAN; IMAGE ANALYSIS; MAMMOGRAPHY; RECEIVER OPERATING CHARACTERISTIC;

BREAST NEOPLASMS; CALCINOSIS; FEMALE; HUMANS; MAMMOGRAPHY; ROC CURVE; SENSITIVITY AND SPECIFICITY;

EID: 34948843453     PISSN: 00071285     EISSN: None     Source Type: Journal    
DOI: 10.1259/bjr/30415751     Document Type: Article

References (51)

1. Majid AS, de Paredes ES, Doherty RD, Sharma NR, Salvador X. Missed breast carcinoma: pitfalls and pearls. Radiographics 2003;23:881-95.
2. Kopans DB. The positive predictive value of mammography. AJR Am J Roentgenol 1992;158:521-6.
3. Kolb TM, Lichy J, Newhouse JH. 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 2002;225:165-75.
4. Bird RE, Wallace TW, Yankaskas BC. Analysis of cancers missed at screening mammography. Radiology 1992;184: 613-17.
5. Van Gils CH, Often JD, Verbeek AL, Hendriks JH, Holland R. Effect of mammographic breast density on breast cancer screening performance: a study in Nijmegen, the Netherlands. J Epidemiol Commun Health 1998;52: 267-71.
6. Cole EB, Pisano ED, Kistner EO, Muller KE, Brown ME, Feig SA, et al. Diagnostic accuracy of digital mammography in patients with dense breasts who underwent problem-solving mammography: effects of image processing and lesion type. Radiology 2003;226:153-60.
7. American Cancer Society. Cancer facts and figures 1998. Atlanta, GA: American Cancer Society, Inc., 1998.
8. Sampat PM, Markey MK, Bovik AC. Computer-aided detection and diagnosis in mammography. In: Bovik AC, editor. Handbook of image and video processing. New York: Academic Press, 2005:1195-217.
9. Markey MK, Lo JY, Floyd CE, Jr. Differences between computer-aided diagnosis of breast masses and that of calcifications. Radiology 2002;223:489-93.
10. Cheng HD, Cai X, Chen X, Hu L, Lou X. Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognit 2003;36:2967-91.
11. Wu Y, Giger ML, Doi K, Vyborny C J, Schmidt RA, Metz CE. Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology 1993;187:81-7.
12. Baker JA, Kornguth PJ, Lo JY, Floyd CE. Artificial neural network: improving the quality of breast biopsy recommendations. Radiology 1996;198:131-5.
13. Lo JY, Baker JA, Kornguth PJ, Iglehart JD, Floyd CE. Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features. Radiology 1997;203:159-63.
14. Shen L, Rangayyan RM, Desautels JEL. Application of shape analysis to mammographic calcifications. IEEE Trans Med Imaging 1994;13:263-74.
15. Jiang Y, Nishikawa RM, Wolverton DE, Metz CE, Giger ML, Schmidt RA, et al. Malignant and benign clustered microcalcifications: automated feature analysis and classification. Radiology 1996;198:671-8.
16. Chan HP, Sahiner B, Lam KL, Petrick N, Helvie MA. Goodsitt M, et al. Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. Med Phys 1998;25:2007-19.
17. Tsujii O, Freedman MT, Mun SK. Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network. Pattern Recognit 1999;32:891-903.
18. Veldkamp WJH, Karssemeijer N, Often JDM, Hendriks JHCL. Automated classification of clustered microcalcifications into malignant and benign types. Med Phys 2000;27:2600-8.
19. Markopoulos C, Kouskos E, Koufopoulos K, Kyriakou V, Gogas J. Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography. Eur J Radiol 2001;39:60-5.
20. Verma B, Zakos J. A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Trans Inform Technol Biomed 2001;5:46-54.
21. De Santo M, Molinara M, Tortorella F, Vento M. Automatic classification of clustered microcalcifications by a multiple expert system. Pattern Recognit 2003;36:1467-77.
22. Lee SK, Chung P, Chang CI, Lo CS, Lee T, Hsu GC, et al. Classification of clustered microcalcifications using a shape cognition neural network. Neural Netw 2003;16:121-32.
23. Kallergi M. Computer-aided diagnosis of mammographic microcalcification clusters. Med Phys 2004;31:314-26.
24. Wei L, Yang Y, Nishikawa RM, Jiang Y. A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans Med Imaging 2005;24:371-80.
25. Papadopoulos A, Fotiadis DI, Likas A. Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artif Intell Med 2005;34:141-50.
26. Dhawan AP, Chitre Y, Kaiser-Bonasso C. Analysis of mammographic microcalcifications using gray-level image structure features. IEEE Trans Med Imaging 1996;15:246-59.
27. Soltanian-Zadeh H, Rafee-Rad F, Pourabdollah-Nejad D. Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms. Pattern Recognit 2004;37:1973-86.
28. Kramer D, Aghdasi F. Texture analysis techniques for the classification of microcalcifications in digitized mammograms. In: Proceedings of the 5th IEEE AFRICON Conference Electrotechnical Service for Africa, Cape Town, 1999.
29. Sakka E, Prentza A, Koutsouris D. Classification algorithms for microcalcifications in mammograms (review). Oncol Rep 2006;15(Spec No):1049-56.
30. Veldkamp WJH, Karssemeijer N. Influence of segmentation on classification of microcalcifications in digital mammography. In: Proceedings of the 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 1996 October 31-November 3. Amsterdam, Netherlands: Institute of Electrical and Electronic Engineers, Inc., 1996.
31. Paquerault S, Yarusso LM, Papaioannou J, Jiang Y, Nishikawa RM. Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance. Med Phys 2004;31:2648-57.
32. Thiele DL, Kimme-Smith C, Johnson TD, McCombs M, Bassett LW. Using tissue texture surrounding calcification clusters to predict benign vs malignant outcomes. Med Phys 1996;23:549-55.
33. Sakellaropoulos P, Costaridou L, Panayiotakis G. An image visualization tool in mammography. Med Inform 1999;24: 53-73.
34. Sakellaropoulos P, Costaridou L, Panayiotakis G. Using component technologies for web based wavelet enhanced mammographic image visualization. Med Inform 2000;25:171-81.
35. Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer P. The Digital Database for Screening Mammography. In: Proceedings of the 5th International Workshop on Digital Mammography, IWDM; 2000 June 11-14; Toronto, Canada. Wisconsin: Medical Physics Publishing, 2000.
36. American College of Radiology. American College of Radiology breast imaging reporting and data system atlas (BI-RADS Atlas). Reston, VA: American College of Radiology (ACR), 2003.
37. Sakellaropoulos P, Costaridou L, Panayiotakis G. A wavelet-based spatially adaptive method for mammographic contrast enhancement. Phys Med Biol 2003;48:787-803.
38. Costaridou L, Sakellaropoulos P, Skiadopoulos S, Panayiotakis G. Locally adaptive wavelet contrast enhancement. In: Costaridou L, editor. Medical image analysis methods. Boca Raton, FL: Taylor & Francis Group LCC, CRC Press, 2005:225-70.
39. Costaridou L, Skiadopoulos S, Karahaliou A, Sakellaropoulos P, Panayiotakis G. On the lesion specific enhancement hypothesis in mammography. In: Proceedings of 14th International Conference of Medical Physics, ICMP; 2005 September 14-17; Nuremberg, Germany. Berlin, Germany: Fachverlag Schiele & Schon GmbH, 2005.
40. Gonzalez RC, Woods RE, editors. Digital image processing. Upper Saddle River, NJ: Prentice-Hall, Inc., 2002.
41. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973;SMC-3:610-21.
42. Walker RF, Jackway P, Longstaff ID. Improving co-occurrence matrix feature discrimination. In: Proceedings of the 3rd Conference on Digital Image Computing: Techniques and Applications (DICTA '95); 1995 December 6-8; Brisbane, Australia, 1995.
43. Galloway M. Texture analysis using gray level run lengths. Comput Graph Image Proc 1975;4:172-9.
44. Laws KI. Texture energy measures. In: Proceedings of DARPA Image Understanding Workshop; Los Angeles, 1979.
45. Theodoridis S, Koutroumbas K, editors. Pattern recognition. Amsterdam: Elsevier Academic Press, 2003.
46. Dudani SA. The distance weighted nearest neighbour rule. IEEE Trans Systems Man Cybern 1976;SMC-6:325-7.
47. Christodoulou CI, Pattichis CS, Pantziaris M, Nicolaides A. Texture-based classification of atherosclerotic carotid plaques. IEEE Trans Med Imaging 2003;22:902-12.
48. Erkel AR, Pattynama PM. Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. Eur J Radiol 1998;27:88-94.
49. Metz CE. Quantification of failure to demonstrate statistical significance. The usefulness of confidence intervals. Invest Radiol 1993;28:59-63.
50. Ursin G, Hovanessian-Larsen L, Parisky YR, Pike MC, Wu AH. Greatly increased occurrence of breast cancers in areas of mammographically dense tissue. Breast Cancer Res 2005;7:R605-8.
51. Kerlikowske K, Grady D, Barclay J, Frankel SD, Ominsky SH, Sickles EA, et al. Variability and accuracy in mammographic interpretation using the American College of Radiology Breast Imaging Reporting and Data System. J Natl Cancer Inst 1998;90:1801-8.