Significance analysis of qualitative mammographic features, using linear classifiers, neural networks and support vector machines

European Journal of Radiology

Volumn 54, Issue 1, 2005, Pages 80-89

  Mavroforakis, Michael ^a,d   Georgiou, Harris ^a   Dimitropoulos, Nikos ^b   Cavouras, Dionisis ^c   Theodoridis, Sergios ^a  

^a UNIVERSITY OF ATHENS   (Greece)

^b Euromedica Medical Center   (Greece)

^c UNIVERSITY OF WEST ATTICA   (Greece)

^d NONE   (Greece)

Author keywords

Automated diagnosis; Mammography; SVM classifiers; Tumor characterization

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; COMPUTER ARCHITECTURE; DATABASE SYSTEMS; DIAGNOSTIC PRODUCTS; IMAGE ANALYSIS; MEDICAL IMAGING; NEURAL NETWORKS; PATTERN RECOGNITION; RADIOLOGY; TUMORS;

CLINICAL DATA; MAMMOGRAPHIC TUMORS; SUPPORT VECTOR MACHINES;

MAMMOGRAPHY;

ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER DIAGNOSIS; CLINICAL FEATURE; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED STUDY; DATA BASE; DIAGNOSTIC VALUE; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; IMAGING SYSTEM; MAMMOGRAPHY; PRIORITY JOURNAL; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; STATISTICAL SIGNIFICANCE;

ANALYSIS OF VARIANCE; BREAST NEOPLASMS; DIAGNOSIS, COMPUTER-ASSISTED; EXPERT SYSTEMS; FEMALE; HUMANS; MAMMOGRAPHY; NEURAL NETWORKS (COMPUTER); RADIOGRAPHIC IMAGE ENHANCEMENT; SENSITIVITY AND SPECIFICITY;

EID: 15744379052     PISSN: 0720048X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejrad.2004.12.015     Document Type: Article

References (25)

1. L. Bocchi, and G. Coppini Tissue characterization from X-ray images Med Eng Phys 19 4 1997 336 342
2. R.L. Eagan Breast imaging: diagnosis and morphology of breast diseases 1988 Saunders Philadelphia
3. S.L. Robbins, M. Angell, and V. Kumar Basic pathology 1981 Saunders Philadelphia
4. I. Christoyianni, E. Dermatas, and G. Kokkinakis Fast detection of masses in computer-aided mammography IEEE Sig Proc Mag 2000 54 64
5. C.D. Orsi, D.J. Getty, and J.A. Swets Reading and decision aids for improved accuracy and standardization of mammographic diagnosis Radiology 184 1992 619 622
6. Y. Wu, M.L. Giger, and K. Doi Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer Radiology 187 1993 81 87
7. L.V. Ackerman, and A.N. Mucciardi Classification of benign and malignant breast tumors on the basis of 36 radiographic properties Cancer 31 1973 342 352
8. Li Huai, Yue Wang, and K.J. Ray Liu Computerized radiographic mass detection. Part I. Lesion site selection by morphological enhancement and contextual segmentation IEEE Trans Med Im 20 4 2001 289 301
9. R.M. Haralick, K. Shanmugam, and I. Dinstein Textural features for image classification IEEE Trans Sys Man Cyb SMC-3 3 1973 610 621
10. R.M. Haralick Statistical and structural approaches to texture Proc IEEE 67 5 1979 786 804
11. M. Galloway Texture analysis using gray level run lengths Comp Graph Im Proc 4 1975 172 179
12. M. Mavroforakis, H. Georgiou, D. Cavouras, N. Dimitropoulos, and S. Theodoridis Mammographic mass classification using textural features and descriptive diagnostic data Proceedings of the 14th International Conference on Digital Signal Processing (DSP-2002) Santorini Greece, 1-3 July 2002
13. L.M. Bruce, and R.R. Adhami Classifying mammographic mass shapes using the wavelet transform modulus-maxima method IEEE Trans Med Im 18 12 1999 1170 1177
14. H. Georgiou, D. Cavouras, N. Dimitropoulos, and S. Theodoridis Mammographic mass shape characterization using neural networks Proceedings of the Second European Symposium on Biomedical Engineering and Medical Physics Patras, Greece, BME-13 2000
15. J. Kilday, F. Palmieri, and M.D. Fox Classifying mammographic lesions using computerized image analysis IEEE Trans Med Im 12 4 1993 664 669
16. H. Georgiou, M. Mavroforakis, D. Cavouras, N. Dimitropoulos, and S. Theodoridis Multiscaled mammographic mass shape analysis and classification using neural networks Proceedings of the 14th International Conference on Digital Signal Processing (DSP-2002) Santorini, Greece, July 1-3 2002
17. Cheeseman P, Stutz J, Bayesian Classification (AutoClass): Theory and Results. NASA Ames Research Center: Artificial Intelligence Research Branch; 1995.
18. S. Theodoridis, and K. Koutroumbas Pattern recognition 2nd ed. 1999 Academic Press USA
19. R. Leeden, K. Vrijburg, and J. Leeuw A review of two different approaches for the analysis of growth data using longitudinal mixed linear models: comparing hierarchical linear regression (ML3, HLM) and repeated measures designs with structured covariance matrices (BMDP5V). The Statistical Software Newsletter SSNinCSDA 20 1996 583 605
20. M. Stone Cross-validatory choice and assessment of statistical predictions JR Stat Soc B36 1 1974 111 147
21. L. Devroye, L. Gyorfi, and G. Lugosi A probabilistic theory of pattern recognition 1996 Springer-Verlag Inc. New York
22. S. Haykin Neural networks: a comprehensive foundation 2nd ed. 1999 Prentice Hall New Jersey
23. T. Poggio, and F. Girosi Networks for approximation and learning Proc. IEEE 78 1990 1481 1497
24. R. Reed Prunning algorithms: a survey IEEE Trans Neural Networks 4 5 1998 740 747
25. N. Christianini, and J. Shawe-Taylor An introduction to support vector machines 2000 Cambridge University Press Cambridge, UK