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

References 

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