Ontology-based knowledge organization for the radiograph images segmentation

Advances in Electrical and Computer Engineering

Volumn 8, Issue 1, 2008, Pages 56-61

  Matei, Oliviu ^a  

^a TECHNICAL UNIVERSITY OF CLUJ NAPOCA   (Romania)

Author keywords

Normalization; Onotologies; Radiographs

Indexed keywords

EID: 70349189190     PISSN: 15827445     EISSN: None     Source Type: Journal    
DOI: 10.4316/aece.2008.01010     Document Type: Article

References (20)

1. Nci ontology, http://www.daml.org/ontologies/318.
2. Galen ontology, http://www.opengalen.org, 2001.
3. S. G. Armato and H. Giger, M. L.and MacMahon. Automated lung segmentation in digitized postero-anterior chest radiographs. Academic Radiology, (4):245-255, 1998.
4. M.S. Brown, L.S. Wilson, B.D. Doust, R.W. Gill, and C. Sun. Knowledgebased method for segmentation and analysis of lung boundaries in chest x-ray images. Computerized Medical Imaging and Graphics, 22:463-477, 1998.
5. JJ Cimino, Hricsak G, Johnson SB, and Clayton PD. Designing an introspective multipurpose controlled medical vocabulary. In Proc 13th AnnuSymp Comput Appl Med Care, pages 513-517, 1989.
6. J. Duryea and J.M. Boone. A fully automatic algorithm for the segmentation of lung fields in digital chest radiographic images. Medical Physics, 2(22):183-191, 1995.
7. B. van Ginneken, A. F. Frangi, J. J. Staal, B. M. ter Haar Romeny, and M. A. Viergever. Active shape model segmentation with optimal features. IEEE Transactions on Medical Imaging, 21(8):924-933, 2002.
8. B. van Ginneken, Haar Romeny B. M. ter Katsuragawa, S., K. Doi, and M. A. Viergever. Automatic detection of abnormalities in chest radiographs using local texture analysis. IEEE Transactions on Medical Imaging, 21(2):139-149, 2002.
9. H Gu, M Halper, J Geller, and Y Perl. Benefits of an object-oriented database representation for controlled medical terminologies. J Am Med Inform Assoc, (6):283303, 1999.
10. L. Li, Y. Zheng, M. Kallergi, and R.A. Clark. Improved method for automatic identification of lung regions on chest radiographs. Academic Radiology, 7(8):629-638, 2001.
11. M. Loog and B. van Ginneken. Supervised segmentation by iterated contextual pixel classification. In In Proceedings 16th International Conference on Pattern Recognition, pages 925-928, 2002.
12. M.F. McNitt-Gray, H.K. Huang, and J.W. Sayre. Feature selection in the pattern classification problem of digital chest radiograph segmentation. IEEE Transactions on Medical Imaging, 14(3):537-547, 1995.
13. 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. Medical Physics, 17(3):342-350, 1990.
14. E. Pietka. Lung segmentation in digital chest radiographs. Journal of Digital Imaging, 2:79-84, 1994.
15. C Rosse, JL Mejino, BR Modayur, R Jakobovits, KP Hinshaw, and JF Brinkley. Motivation and organizational principles for anatomical knowledge representation: the digital anatomist symbolic knowledge base. J Am Med Inform Assoc, (5):17-40, 1998.
16. C Rosse, LG Shapiro, and JF Brinkley. The digital anatomist foundational model: principles for defining and structuring its concept domain. In Proc AMIA Symp, pages 820-824, 1998.
17. O. Tsujii, M.T. Freedman, and S.K. Mun. Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network. Medical Physics, 25(6):998-1007, 1998.
18. N.F. Vittitoe, R. Vargas-Voracek, and C.E. Floyd Jr. Identification of lung regions in chest radiographs using markov random field modeling. Medical Physics, 25(6):976-985, 1998.
19. X.W. Xu and K. Doi. Image feature analysis for computer-aided diagnosis: accurate determination of ribcage boundary in chest radiographs. Medical Physics, 5(22):617-626, 1995.
20. X.W. Xu and K. Doi. Image feature analysis for computer-aided diagnosis: detection of right and left hemidiaphragm edges and delineation of lung field in chest radiographs. Medical Physics, 9(23):1613-1624, 1996.