A computer-aided detection system for clustered microcalcifications

References 

1. El-Naqa I, Yang Y, Wernick M, Galatsanos N, Nishikawa R. A support vectormachine approach for detection of microcalcifications. IEEE Transactions on Medical Imaging. 2002;21(12):1552–1563
   • MEDLINE
   • CrossRef
2. Tsujii O, Freedman MT, Mun SK. Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network. Pattern Recognition. 1999;32:891–903
3. Wei L, Yang Y, Nishikawa R, Jiang Y. A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Transactions on Medical Imaging. 2005;24(3):371–380
   • MEDLINE
   • CrossRef
4. Veldkamp W, Karssemeijer N. Improved correction for signal dependent noise applied to automatic detection of microcalcifications. In:  Karssemeijer N,  Thijssen M,  Hendriks J,  van Erning L editor. Digital mammography, vol. 98. Nijmegen: Kluwer Academic; 1998;p. 160–176
5. D’Elia C, Poggi G, Scarpa G. A tree-structured Markov random field model for Bayesian image segmentation. IEEE Transactions on Image Processing. 2003;12(10):1259–1273
6. Cheng H, Cai X, Chen X, Hu L, Lou X. Computer-aided detection and classification of microcalcification in mammograms: a survey. Pattern Recognition. 2003;36:2967–2991
7. Nishikawa R. Current status and future directions of computer-aided diagnosis in mammography. Computerized Medical Imaging and Graphics. 2007;31:1357–1376
8. Wu Y, Giger M, Doi K, Vyborny C, Schmidt R, Metz C. Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology. 1993;187:81–87
   • MEDLINE
9. Wei L, Yang Y, Nishikawa R, Wernick M, Edwards A. Relevance vector machine for automatic detection of clustered microcalcifications. IEEE Transactions on Medical Imaging. 2005;24(10):1278–1285
   • MEDLINE
   • CrossRef
10. Jiang J, Yao B, Wason A. A genetic algorithm design for microcalcification detection and classification in digital mammograms. Computerized Medical Imaging and Graphics. 2007;31:49–61
11. Peng Y, Yao B, Jiang J. Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis. Artificial Intelligence in Medicine. 2006;37:43–53
    • Abstract
    • Full Text
    • Full-Text PDF (483 KB)
    • CrossRef
12. Nakayama R, Uchiyama Y, Yamamoto K, Watanabe R, Namba K. Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms. IEEE Transactions on Biomedical Engineering. 2006;53(2):273–283
    • MEDLINE
    • CrossRef
13. Strickland R, Hahn H. Wavelet transform for detecting microcalcifications in mammograms. IEEE Transactions on Medical Imaging. 1996;15(2):218–229
    • CrossRef
14. Netsch T, Peitgen H. Scale-space signatures for the detection of clustered microcalcifications in digital mammograms. IEEE Transaction on Medical Imaging. 1999;18(9):774–786
    • MEDLINE
    • CrossRef
15. Gurcan M, Yardimci Y, Cetin A, Ansari RR. Detection of microcalcifications in mammograms using higher order statistics. IEEE Signal Processing Letters. 1997;4(8):213–216
16. Li S. Markov random field modeling in image analysis. 3rd edition. Springer; 2009;
17. Held K, Kops E, Krause B, Wells WM, Kikinis R, Muller-Gartner H. Markov random field segmentation of brain mr images. IEEE Transactions on Medical Imaging. 1997;16(6):878–886
    • MEDLINE
    • CrossRef
18. Katartzis A, Sahli H, Cornelis J, Fotopoulos S, Panayiotakis G. Model-based technique for the measurement of skin thickness in mammography. Medical and Biological Engineering and Computing. 2002;40(2):153–162
19. Medina R, Garreau M, Toro J, Breton HL, Coatrieux JL, Jugo D. Markovrandom field modeling for three-dimensional reconstruction of the left ventricle in cardiac angiography. IEEE Transactions on Medical Imaging. 2006;25(8):1087–1100
    • MEDLINE
    • CrossRef
20. Li H, Kallergi M, Clarke L, Jain V, Clark R. Markov random field for tumor detection in digital mammography. IEEE Transactions on Medical Imaging. 1995;14(3):565–576
    • CrossRef
21. Suliga M, Deklerck R, Nyssen E. Markov random field-based clustering applied to the segmentation of masses in digital mammograms. Computerized Medical Imaging and Graphics. 2008;32:502–512
22. Zheng L, Chan K. An artificial intelligent algorithm for tumor detection inscreening mammogram. IEEE Transactions on Medical Imaging. 2001;20(7):559–567
    • MEDLINE
    • CrossRef
23. Yu S, Li K, Huang YK. Detection of microcalcifications in digital mammograms using wavelet filter and markov random field model. Computerized Medical Imaging and Graphics. 2006;30:163–173
24. Huang Y, Yu S. Recognition of microcalcifications in digital mammograms based on markov random field and deterministic fractal modeling. In: 29th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007). 2008;
25. Cheng HD, Wang J, Shi X. Microcalcification detection using fuzzy logic and scale space approach. Pattern Recognition. 2004;37:363–375
26. Papadopoulos A, Fotiadis D, Likas A. Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artificial Intelligence in Medicine. 2005;34:141–150
    • Abstract
    • Full Text
    • Full-Text PDF (328 KB)
    • CrossRef
27. De Santo M, Molinara M, Tortorella F, Vento M. Automatic classification of clustered microcalcifications by a multiple expert system. Pattern Recognition. 2003;3:1467–1477
28. Marrocco C, Molinara M, Tortorella F. Algorithms for detecting clusters of microcalcifications in mammograms. In:  Roli F,  Vitulano S editor. 13th International Conference on Image Analysis and Processing (ICIAP 2005), Vol. 3617 of LNCS. Berlin: Springer; 2005;p. 884–892
29. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics. 1979;9(1):62–66
30. Poggi G, Scarpa G, Zerubia J. Supervised segmentation of remote sensing images based on a tree-structured mrf model. IEEE Transactions on Geoscience and Remote Sensing. 2005;43(8):1901–1911
31. Weiss G, Provost F. The effect of class distribution on classifier learning: an empirical study, Tech. Re ML-TR-44, Department of Computer Science, Rutgers University, 2001.
32. Chawla NV, Japkowicz N, Kotcz A. Editorial: special issue on learning from imbalanced data sets, SIGKDD Exploration Newsletter. ACM. 2004;6(1):1–6
33. Molinara M, Ricamato M, Tortorella F. Facing imbalanced classes through aggregation of classifiers. In:  Cucchiara R editors. 14th international conference on image analysis and processing (ICIAP 2007). IEEE Computer Society; 2007;p. 43–48
34. Duda R, Hart P, Stork D. Pattern classification. 2nd edition. John Wiley & Sons; 2001;
35. Teague M. Image analysis via the general theory of moments. Journal of Optical Society of America. 1980;70:920–930
36. Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. The Annals of Statistics. 2000;38:337–374
37. Karssemeijer N. Adaptive noise equalization and recognition of microcalcification clusters in mammograms. International Journal of Pattern Recognition and Artificial Intelligence. 1993;7:1357–1376