Breast cancer diagnosis in digitized mammograms using curvelet moments

Computers in Biology and Medicine

Volumn 64, Issue , 2015, Pages 79-90

  Dhahbi, Sami ^a   Barhoumi, Walid ^a   Zagrouba, Ezzeddine ^a  

^a UNIVERSITY OF MANOUBA   (Tunisia)

Author keywords

Breast cancer diagnosis; Curvelet moments; Curvelet transform; Feature reduction; Mammography

Indexed keywords

DATABASE SYSTEMS; DIAGNOSIS; DISEASES; EXTRACTION; FEATURE EXTRACTION; MAMMOGRAPHY; NEAREST NEIGHBOR SEARCH; STATISTICAL METHODS; X RAY SCREENS;

BREAST CANCER DIAGNOSIS; COMPUTER AIDED DIAGNOSIS SYSTEMS; CURVELET TRANSFORMS; CURVELETS; DIGITAL DATABASE FOR SCREENING MAMMOGRAPHIES; FEATURE REDUCTION; K-NEAREST NEIGHBOR CLASSIFIER; LEAVE-ONE-OUT CROSS VALIDATIONS;

COMPUTER AIDED DIAGNOSIS;

ARTICLE; BREAST; BREAST CANCER; DATA BASE; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DIGITAL MAMMOGRAPHY; HUMAN; HUMAN TISSUE; PRIORITY JOURNAL; THEORY; VALIDATION STUDY; ALGORITHM; BREAST TUMOR; COMPUTER ASSISTED DIAGNOSIS; FACTUAL DATABASE; FEMALE; MAMMOGRAPHY; PROCEDURES; RADIOGRAPHY; REPRODUCIBILITY;

ALGORITHMS; BREAST NEOPLASMS; DATABASES, FACTUAL; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAMMOGRAPHY; REPRODUCIBILITY OF RESULTS;

EID: 84934780081     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2015.06.012     Document Type: Article

References (38)

1. J. Ferlay, I. Soerjomataram, M. Ervik, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. Parkin, D. Forman, F. Bray, Globocan, v1.0, cancer incidence and mortality worldwide, 2009. Available from: accessed on 25/04/2015. 〈,〉. http://globocan.iarc.fr.
2. Jotwani A., Gralow J. Early detection of breast cancer. Mol. Diagn. Ther. 2009, 13(6):349-357.
3. Nelson H., Tyne K., Naik A., Bougatsos C., Chan B., Nygren P., Humphrey L. Screening for breast cancer. systematic evidence review update for the U. S. preventive services task force. Ann. Intern. Med. 2009, 151(10):w242-727.
4. Hofvind S., Ursin G., Tretli S., SebuodegÅrd S., Moller B. Breast cancer mortality in participants of the Norwegian breast cancer screening program. Cancer 2013, 119:3106-3112.
5. Dinnes J., Moss S., Melia J., Blanks R., Song F., Kleijnen J. Effectiveness and cost-effectiveness of double reading of mammograms in breast cancer screening. findings of a systematic review. The Breast 2001, 10(6):455-463.
6. Astley S., Gilbert F. Computer-aided detection in mammography. Clin. Radiol. 2004, 59(5):390-399.
7. Moura D., Guevara López M. An evaluation of image descriptors combined with clinical data for breast cancer diagnosis. Int. J. Comp. Ass. Rad. Surg. 2013, 8(4):561-574.
8. Murtagh F., Starck J. Wavelet and curvelet moments for image classification. application to aggregate mixture grading. Pattern Recognit. Lett. 2008, 29:1557-1564.
9. S. Gardezi, I. Faye, M. Eltoukhy, Analysis of mammogram images based on texture features of curvelet subbands, in: Fifth International Conference on Graph and Image Proceedings, 2014.
10. F. Narváez, G. Díaz, F. Gómez, E. Romero, A content-based retrieval of mammographic masses using the curvelet descriptor, in: Proceedings of SPIE, Medical Imaging 8315, 2012, pp. 1-7.
11. Rashed E., Ismail I., Zaki S. Multiresolution mammogram analysis in multilevel decomposition. Pattern Recognit. Lett. 2007, 28(2):286-292.
12. Eltoukhy M., Ibrahima F., Samir B. Breast cancer diagnosis in digital mammogram using multiscale curvelet transform. Comput. Med. Image Gr. 2010, 34(4):269-276.
13. Eltoukhy M., Ibrahima F., Samir B. A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram. Comput. Biol. Med. 2010, 40(4):384-391.
14. Jianwei M. Improved iterative curvelet thresholding for compressed sensing and measurement. IEEE Trans. Instrum. Meas. 2011, 60(1):126-136.
15. Eltoukhy M., Ibrahima F., Samir B. A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation. Comput. Biol. Med. 2012, 42(1):123-128.
16. Gedik N., Atasoy A. A computer-aided diagnosis system for breast cancer detection by using a curvelet transform. Turk. J. Elec. Eng. Comp. Sci 2013, 21(4):1002-1014.
17. M. Eltoukhy, J. Safdar, G. ans Syed, I. Faye, A method to reduce curvelet coefficients for mammogram classification, in: Region 10 IEEE Symposium, 2014, pp. 663-666.
18. Candès E., Donoho D. Curvelets. a surprisingly effective nonadaptive representation for objects with edges. Curves Surf. 2000, 105-120.
19. Li L., Zhanga X., Zhanga H., Hea X., Xua M. Feature extraction of non-stochastic surfaces using curvelets. Precis. Eng. 2015, 39:212-219.
20. Dettori L., Semler L. A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography. Comput. Biol. Med 2007, 37(4):486-498.
21. Candès E., Demanet L., Donoho D., Ying L. Fast discrete curvelet transforms. Multiscale Model. Simul. 2006, 5(3):861-899.
22. Dalfo C., Fiol M., Garriga E. Moments in graphs. Discrete Appl. Math. 2013, 161(6):768-777.
23. Jarque C., Bera A. A test for normality of observations and regression residuals. Int. Stat. Rev. 1987, 55(2):163-172.
24. Liao S., Pawlak M. On image analysis by moments. IEEE Trans. Pattern Anal. Mach. Intell. 1996, 18:254-266.
25. Roy A., Das N., Saha A., Sarkar R., Basu S., Kundu M., Nasipuri M. A comparative study of feature ranking methods in recognition of handwritten numerals. Adv. Intell. Syst. Comput. 2015, 324:473-479.
26. Breiman L. Bagging predictors. Mach. Learn. 1996, 24:123-140.
27. Preece S., Goulermas J., Kenney L., Howard D. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans. Biomed. Eng. 2009, 56(3):871-879.
28. Yao Z., Ruzzo W. A regression-based k nearest neighbor algorithm for gene function prediction from heterogeneous data. Bioinformatics 2006, 7:1-11.
29. Dudani S.A. The distance-weighted k-nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC 1976, 6(4):325-327.
30. J. Suckling, J. Parker, D. Dance, S. Astley, I. Hutt, C. Boggis, I. Ricketts, E. Stamatakis, N. Cerneaz, S. Kok, P. Taylor, D. Betal, J. Savage, The mammographic image analysis society digital mammogram database, in: Proceedings of the International Workshop on Digital Mammography, Exerpta Medica, International Congress Series, York-England, 1069, 1994, pp. 375-378.
31. Li Q. Improvement of bias and generalizability for computer-aided diagnostic schemes. Comput. Med. Imaging Gr. 2007, 31:338-345.
32. Manocha S., Girolami M. An empirical analysis of the probabilistic k-nearest neighbour classifier. Pattern Recognit. Lett. 2007, 28:1818-1824.
33. Hand D., Vinciotti V. Choosing k for two-class nearest neighbour classifiers with unbalanced classes. Pattern Recognit. Lett. 2003, 24:1555-1562.
34. Deepa S., Bharathi V. Textural feature extraction and classification of mammogram images using cccm and pnn. J. Comput. Eng. 2013, 10(6):7-13.
35. M. Heath, K. Bowyer, D. Kopans, W. Kegelmeyer, R. Moore, K. Chang, S. Munishkumaran, Current status of the digital database for screening mammography, in: Digital Mammography, Springer, The Netherlands, 1998, pp. 457-460.
36. Jiang M., Zhang S., Li H., Metaxas N. Computer-aided diagnosis of mammographic masses using scalable image retrieval. IEEE Trans. Biomed. Eng. 2015, 62(2):783-792.
37. S. Varma, R. Simon, Bias in error estimation when using cross-validation for model selection, BMC Bioinform. 91 (7), 2006.
38. Karahaliou A., Boniatis I., Skiadopoulos S., Sakellaropoulos F., Arikidis N., Likaki E., Panayiotakis G., Costaridou L. Breast cancer diagnosis. analyzing texture of tissue surrounding microcalcifications. IEEE Trans. Inf. Technol. Biomed. 2008, 12(6):731-738.