Detection and recognition of lung nodules in spiral CT images using deformable templates and bayesian post-classification

Proceedings - International Conference on Image Processing, ICIP

Volumn 2, Issue , 2004, Pages 2921-2924

  Farag, Aly ^a   El Baz, Ayman ^a   Gimel'farb, G ^b   Falk, Robert ^c  

^a University of Louisville   (United States)

^b UNIVERSITY OF AUCKLAND   (New Zealand)

^c JEWISH HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FEATURE EXTRACTION; IMAGE ANALYSIS; IMAGE SEGMENTATION; MATHEMATICAL MODELS; REGRESSION ANALYSIS;

BAYESIAN CLASSIFICATION; FALSE POSITIVE NODULES (FPN); LINEAR COMBINATION OF GAUSSIANS (LCG); LUNG ABNORMALITIES;

COMPUTERIZED TOMOGRAPHY;

EID: 20444450507     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. A. A. Farag, A. El-Baz, and G.L.Gimel'farb, "Precise Image Segmentation by Iterative EM-Based Approxiation of Empirical Grey Level Distributions with Linear Combinations of Gaussions", in IEEE Workshop on Learning in Computer Vision and Pattern Recognition, Washington, DC, USA, July, 2004.
2. G.LGimel'farb, A. A. Farag, and A. El-Baz, "ExpectationMaximization for a Linear Combination of Gaussians", in Proc. IEEE Int. Conf. on Pattern Recognition (ICPR 2004), Cambridge, United Kingdom, August, 2004.
3. P. M. Boiselle and C. S. White (Eds.) New Techniques in Thoracic Imaging. M. Dekker, New York, 2002.
4. L. Quekel, A.Kessels, R. Goei, and J. V. Engelshoven, "Miss rate of lung cancer on the chest radiograph in clinical practice," Chest, Vol. 115, no. 3, pp. 720-724, 1999.
5. S.-C. B. Lo, M. T. Freedman, J.-S. Lin, and S. K. Mun, "Automatic lung nodule detection using profile matching and back-propagation neural network techniques," J. Digital Imaging, Vol. 6, no. 1, pp. 48-54, 1993.
6. F. Mao, W. Qian, J. Gaviria, and L. Clarke, "Fragmentary window filtering for multiscale lung nodule detection," Academic Radiology, Vol. 5, no. 4, pp. 306-311, 1998.
7. X. Xu, S. Katsuragawa, K. Ashizawa,H. MacMahon, and K. Doi, "Analysis of image features of histograms of edge gradient for false positive reduction in lung nodule detection in chest radiographs," Proc. SPIE, Vol. 3338, pp. 318-326, 1998.
8. W. Lampeter, "ANDS-V1 computer detection of lung nodules," Proc. SPIE, Vol. 555, pp. 253-261, 1985.
9. M. J. Carreira, D. Cabello, M. G. Penedo,and J. M. Pardo, "Computer aided lung nodule detection in chest radiography," in R. T. Chin et al. (Eds.) Image Analysis Applications and Computer Graphics, (Lecture Notes in Computer Science 1024), Springer, Berlin, pp. 331-338, 1995.
10. J. S. Lin, S. B. Lo, A. Hasegawa, M. T. Freedman, and S. K. Mun, "Reduction of false positives in lung nodule detection using a two-level neural classification," IEEE Trans. Med. Imaging, Vol. 15, pp. 206-216, April 1996.
11. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, Mass., 1989.
12. R. H. Myers, D. C. Montgomery, and G. G. Vining, Generalized Linear Models, John Wiley & Sons, Inc., New York, 2002.