Identification of lung regions in chest radiographs using Markov random field modeling

Medical Physics

Volumn 25, Issue 6, 1998, Pages 976-985

  Vittitoe, Neal F ^a,b   Vargas Voracek, Rene ^b   Floyd, Carey E ^b  

^a Duke University   (United States)

^b DUKE UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

Chest radiography; Computer aided diagnosis; Markov random fields

Indexed keywords

ABILITY TESTING; BIOLOGICAL ORGANS; MARKOV PROCESSES; PIXELS; RADIOGRAPHY;

CHEST RADIOGRAPHS; CHEST RADIOGRAPHY; ITERATED CONDITIONAL MODES; LUNG REGIONS; MARKOV RANDOM FIELD MODEL; MARKOV RANDOM FIELDS; REGION TYPES; SPATIAL INFORMATIONS; TEXTURAL INFORMATION; TRAINING SETS;

COMPUTER AIDED DIAGNOSIS;

ACCURACY; ALGORITHM; ARTICLE; COMPUTER ASSISTED DIAGNOSIS; COMPUTER PROGRAM; DIGITAL RADIOGRAPHY; PRIORITY JOURNAL; PROBABILITY; THORAX RADIOGRAPHY;

EID: 0031859872     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.598405     Document Type: Article

References (25)

1. A. H. Baydush and C. E. Floyd, Jr., "Spatially varying Bayesian image estimation," Acad. Radiol. 3, 129-136 (1996).
2. J. Duryea and J. M. Boone, "A fully automated algorithm for the segmentation of lung fields on digital chest radiographic images," Med. Phys. 22, 183-191 (1995).
3. S. Katsuragawa, K Doi, H. MacMahon, N. Nakamori, Y. Sasaki, and J. J. Fennessy, "Quantitative computer-aided analysis of lung texture in chest radiographs," Radiographics 10, 257-269 (1990).
4. R. Vargas-Voracek and C. E. Floyd, Jr., "Computer-aided diagnosis of interstitial lung disease: A probabilistic texture-based analysis," Radiology P197, 425 (1995).
5. M. L. Giger, N. Ahn, K. Doi, H. MacMahon, and C. E Metz, "Computerized detection of pulmonary nodules in digital chest images: Use of morphological filters in reducing false-positive detections," Med. Phys. 17, 861-865 (1990).
6. N. F. Vittitoe, J. A. Baker, and C. E. Floyd, Jr., "Fractal texture analysis in computer-aided diagnosis of solitary pulmonary nodules," Acad. Radiol. 4, 96-101 (1997).
7. N. Nakamori, K. Doi, V. Sabeti, and H. MacMahon, "Image feature analysis and computer-aided diagnosis in digital radiography automated analysis of sizes of heart and lung in chest images," Med. Phys. 17, 342-350 (1990).
8. M. F. McNitt-Gray, H. K. Huang, and J. W. Sayre, "Feature selection in the pattern classification problem of digital chest radiography segmentation," IEEE Trans. Med. Imaging 14, 537-547 (1995).
9. E Pietka, "Lung segmentation in digital radiographs," J. Digit. Imaging 7, 79-84 (1994).
10. H. G. Chotas and C. E. Ravin, "Chest radiography: estimated lung volume and projected area obscured by the heart, mediastinum, and diaphragm," Radiology 193, 403-404 (1994).
11. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images," IEEE PAMI 6, 721-741 (1984)
12. P. L. Dobruschin, "The description of a random field by means of conditional probabilities and conditions of its regularity," Theor. Probab. Appl. 13, 197-224 (1968).
13. J. Besag, "Spatial interaction and the statistical analysis of lattice systems," J. R. Statist Soc B 36, 192-236 (1974).
14. C. C. Chen and C. L. Huang, "Markov random fields for texture classification," Prog. Inorg. Chem. 14, 907-914 (1993).
15. H. Derin and H Elliott, "Modeling and segmentation of noisy and textured images using Gibbs random fields," IEEE PAMI 9, 39-55 (1987).
16. R. Chellappa and A. Jain, Eds., Markov Random Fields: Theory and Application (Academic, New York, 1991)
17. N. Karssemeijer, "Application of Bayesian methods to segmentation in medical images," in Stochastic Models, Statistical Methods, and Algorithms in Image Analysis: Proceedings of the Special Year on Image Analysis held in Rome, Italy, 1990, Lecture Notes in Statistics (Springer-Verlag. New York, 1990), Vol. 74, pp. 203-218.
18. H. D Li, M. Kallergi, L. P. Clarke, V. K. Jain, and R. A. Clark, "Markov random field for tumor detection in digital mammography," IEEE Trans. Med. Imaging 14, 565-576 (1995).
19. R. Vargas-Voracek, "Hierarchical Markov random field modeling for texture analysis and classification in radiographic image processing," Ph. D. Dissertation, Duke University, 1996.
20. R. Vargas-Voracek, C. E. Floyd, Jr., and L. W. Nolle, "Markov random field model estimation using the canonical Gibbs potential definition," IEEE PAMI (submitted).
21. J. Besag, "On the statistical analysis of dirty pictures," J. R. Statist. Soc. B 48, 259-302 (1986).
22. J. Besag, P. Green, D. Higdon, and K. Mengersen, "Bayesian computation and stochastic systems," Stat. Sci. 10, 3-66 (1995)
23. S. Z. Li, Markov Random Field Modeling in Computer Vision (Springer-Verlag, New York, 1995).
24. G. Winkler, Image analysis, random fields and dynamic Monte Carlo methods (Springer-Vertag, New York, 1995).
25. Y. Pao, Adaptive Pattern Recognition and Neural Networks (Addison-Wesley, New York, 1989).