Feature vectors generation for detection of microcalcifications in digitized mammography using neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2687, Issue , 2003, Pages 583-590

  Vega Corona, Antonio ^a   Álvarez Vellisco, Antonio ^b   Andina, Diego ^b  

^a UNIVERSITY OF GUANAJUATO   (Mexico)

^b UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CALCIFICATION (BIOCHEMISTRY); DETECTORS; LABELS; NEURAL NETWORKS;

ADAPTIVE TECHNIQUE; AUTOMATIC DETECTION; FEATURE VECTORS; GRAY-LEVEL; MICROCALCIFICATIONS; MULTI-SCALE WAVELET; REGIONS OF INTEREST; STATISTICAL TECHNIQUES; GRAY LEVELS;

FEATURE EXTRACTION; CALCIFICATION (BIOCHEMISTRY);

EID: 32844464607     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44869-1_74     Document Type: Article

References (13)

1. Cancer facts and figures. American Cancer Society, Atlanta, GA., 1993.
2. D. B.Kopans. Breast Imaging. Lippincott-Raven Publishers, Philadelphia, 1998.
3. S. B. Buchbinder, I. S. Leichter, R. B. Lederman, B. Novak, P. N. Banberg, H. Coopersmith, and S.I. Fields. Can the size of microcalcifications predict malignancy of clusters at mammography? Academic Radiology, 9(1):18-25, September 2002.
4. University of South Florida. Digital database for screrening mammography. ftp://figment.csee.usf.edu/pub/DDSM/cases/, December 2001.
5. R.M.Naga, M.R.Rangaraj, and J.E.Leo. Gradient and texture analysis for the classification of mammographic masses. IEEE Trans. on med. Imaging, 19(10):1032-1043, 2000.
6. A. F.Laine, Schuler, S. J.Fan, and W.Huda. Mammographic feature enhancement by multiscale analysis. IEEE Trans. Medical Imaging, 13(8):725-740, 1994.
7. W. J. H.Veldkamp and N.Karssemeijer. Normalization of local contrast in mammograms. IEEE Transactions on Medical Imaging, 19(7):731-738, 2000.
8. Yu.Songyang and G.Ling. A cad system for automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Transactions on Medical Imaging, 2(2):115-126, 2000.
9. T. C.Wang and N. B.Karayiannis. Detection of microcalcifications in digital mammograms. IEEE Trans. Med. Imaging, 17(4):498-509, 1998.
10. I.Daubechies, M.Antonini, M.Barlaud, and P.Mathieu. Image coding using wavelet transform. IEEE Transactions on Image Processing, 1(2):205-220, 1992.
11. D.Andina and A.Vega. Detection of microcalcifications in mammograms by the combination of a neural detector and multiscale feature enhancement. Bio-Inspired Applications of Connectionism. Lecture Notes in Computer Science. Springer-Verlag, 2085:385-392, 2001.
12. L. M. Belue and Jr. K. W. Bauer. Determining input feature for multilayer perceptrons. Neurocomputing, 7(2):111-122, 1995.
13. D. F.Specht. A general regression neural network. IEEE Transactions on Neural Networks, 2(6):568-576, 1991.