Classification of breast masses in mammograms using genetic programming and feature selection

Medical and Biological Engineering and Computing

Volumn 44, Issue 8, 2006, Pages 683-694

  Nandi, R J ^a   Nandi, A K ^a   Rangayyan, R M ^b   Scutt, D ^a  

^a UNIVERSITY OF LIVERPOOL   (United Kingdom)

^b UNIVERSITY OF CALGARY   (Canada)

Author keywords

Breast cancer; Breast masses; Breast tumors; Computer aided diagnosis; Feature selection; Genetic programming; Pattern classification

Indexed keywords

COMPUTER AIDED DIAGNOSIS; FEATURE EXTRACTION; ONCOLOGY; PATTERN RECOGNITION; TUMORS;

BREAST CANCER; GENETIC PROGRAMMING (GP); KOLMOGOROV-SMIRNOV TEST; KULLBACK-LEIBLER DIVERGENCE;

MAMMOGRAPHY;

ACCURACY; ARTICLE; BREAST CANCER; BREAST TUMOR; CANCER CLASSIFICATION; CANCER SCREENING; COMPUTER AIDED DESIGN; EARLY DIAGNOSIS; GENETIC ALGORITHM; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; KOLMOGOROV SMIRNOV TEST; MAMMOGRAPHY; STATISTICAL ANALYSIS; STUDENT T TEST;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BREAST DISEASES; BREAST NEOPLASMS; FEMALE; HUMANS; MAMMOGRAPHY; MATHEMATICS; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS;

EID: 33746947798     PISSN: 01400118     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11517-006-0077-6     Document Type: Article

References (39)

1. Page title: Breast Cancer Statistics (2005) Source: UK National Statistics website http://www.statistics.gov.uk/
2. Yaffe MJ (2001) Digital mammography: IWDM 2000, Madison. Medical Physics Publishing, WI
3. Peitgen H-O (2003) Digital mammography: IWDM 2002. Springer, Bremen
4. Rangayyan RM, Ayres FJ, Desautels JEL (2005) Computer-aided diagnosis of breast cancer: Toward the detection of early and subtle signs, the 1st world experts' congress on women's health medicine and healthcare. World Academy of Biomedical Technologies, Paris
5. Brzakovic D, Luo XM, Brzakovic P (1990) An approach to automated detection of tumours in mammograms. IEEE Trans Med Imaging 9(3):233-241
6. Kegelmeyer WP, Pruneda Jr JM, Bourland PD, Hillis A, Riggs MW, Nipper ML (1994) Computer-aided mammographic screening for spiculated lesions. Radiology 191(2):331-337
7. Laws KI (1980) Rapid texture identification. In: Proceedings of SPIE, vol 238: Image processing for missile guidance, pp 376-380
8. Rangayyan RM, Mudigonda NR, Desautels JEL (2000) Boundary modeling and shape analysis methods for classification of mammographic masses. Med Biol Eng Comput 38:487-95
9. Sahiner BS, Chan H-P, Petrick N, Helvie MA, Hadjiiski LM (2001) Improvement of mammographic mass characterization using spiculation measures and morphological features. Med Phys 28(7):1455-1465
10. Sahiner BS, Chan H-P, Petrick N, Helvie MA, Goodsitt MM (1998) Computerized characterization of masses on mammograms: The rubber band straightening transform and texture analysis. Med Phys 25(4):516-526
11. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610-621
12. Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786-804
13. Shen L, Rangayyan RM, Desautels JEL (1993) Detection and classification of mammographic calcifications. Int J Pattern Recognit Artif Intell 7(6):1403-1416
14. Rangayyan RM, El-Faramawy NM, Desautels JEL, Alim OA (1997) Measures of acutance and shape for classification of breast tumors. IEEE Trans Med Imaging 16(6):799-810
15. Sahiner BS, Chan HP, Petrick N, Wagner RF, Hadjiiski L (2000) Feature selection and classifier performance in computer-aided diagnosis: The effect of finite sample size. Med Phys 27(7):1509-1522
16. Alto H, Rangayyan RM, Desautels JEL (2005) Content-based retrieval and analysis of mammographic masses. J Electron Imaging 14(2): Article no. 023016, pp 1-17
17. Theodoridis S, Koutroumbas K (2005) Pattern recognition. Academic, New York
18. Pearson K (1901) Principal components analysis. Lond Edinburgh Dublin Philos Mag J Sci 2(2):559
19. Alberta Cancer Board (2004) Screen test: Alberta Program for the early detection of breast cancer, 2001/2003 biennial report, Edmonton, Alberta. http://www.cancerboard.ab.ca/screentest/
20. Mudigonda NR, Rangayyan RM, Desautels JEL (2000) Gradient and texture analysis for the classification of mammographic masses. IEEE Trans Med Imaging 19(10):1032-1043
21. Mudigonda NR, Rangayyan RM, Desautels JEL (2001) Detection of breast masses in mammograms by density slicing and texture flow field analysis. IEEE Trans Med Imaging 20(12):1215-1227
22. Koza JR (1992) Genetic programming: On the programming of computers by means of natural selection. MIT Press, Cambridge, USA
23. Zhang L, Jack LB, Nandi AK (2005) Fault detection using genetic programming. Mech Syst Signal Process 19:271-289
24. Guo H, Jack LB, Nandi AK (2005) Feature generation using genetic programming with application to fault classification. IEEE Trans Syst Man Cybern Part B 35(1):89-99
25. Nordin P, Banzhaf W (1997) Real time control of a khepera robot using genetic programming. Cybern Control 26(3):533-561
26. Kishore JK, Patnaik LM, Mani V, Agrawal VK (2000) Application of genetic programming for multicategory pattern classification. IEEE Trans Evol Comput 4(3):242-258
27. Kudo M, Sklansky J (2000) Comparison of algorithms that select features for pattern classifiers. Pattern Recognit 33(1):25-41
28. Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1989) Numerical recipes in C. Cambridge University Press, Cambridge, UK
29. Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Statist 22(1):79-86
30. Nykter M (2004) Feature selection for Lymphoma outcome prediction. In: Proceedings of the 2nd TICSP workshop on computational systems biology. WCSB '2004, Silja Opera, Helsinki-St. Petersburg 14-16 June, pp 51-52
31. Koller D, Shami M (1996) Toward optimal feature selection. In: Proceedings of the 13th international conference on machine learning. ICML-96, pp 284-292
32. Levner I (2005) Feature selection and nearest centroid classification for protein mass spectrometry. BMC Bioinf 6:68. doi: 10.1186/ 1471-2105-6-68
33. Sahiner B, Chan HP, Petrick N, Helvie MA, Goodsitt MM, Adler DA (1996) Classification of mass and normal breast tissue: Feature selection using a genetic algorithm. In: Proceedings of 3rd internatrional workshop on digital mammography, Chicago, pp 379-384
34. American College of Radiology (ACR) (1998) Illustrated breast imaging reporting and data system (BI-RADS), 3rd edn. American College of Radiology, Reston
35. Fukunaga K, Hayes RR (1989) Effects of sample size in classifier design. IEEE Trans Pattern Anal Mach Intell 11(8):873-885
36. Raudys SJ, Jain AK (1991) Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans Pattern Anal Mach Intell 13(3):252-264
37. Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New York
38. Efron B, Tibshirani RJ (1998) An introduction to the bootstrap. CRC Press LLC, Boca Raton
39. Liu Y, Smith MR, Rangayyan RM (2004) The application of Efron's bootstrap methods in validating feature classification using artificial neural networks for the analysis of mammographic masses. In: 26th annual international conference of the IEEE engineering in medicine and biology society, San Francisco. IEEE, CA, pp 1553-1556