Color to grayscale staining pattern representation in IIF

Proceedings - IEEE Symposium on Computer-Based Medical Systems

Volumn , Issue , 2011, Pages

  Cordelli, Ermanno ^a   Soda, Paolo ^a  

^a UNIVERSITY CAMPUS BIO MEDICO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR CAMERAS; COLOR IMAGES; COLOR MODELS; CONVERSION METHODS; DATA SETS; GRAY SCALE; GREEN BAND; GREYSCALE; INDIRECT IMMUNOFLUORESCENCE; PATTERN REPRESENTATION; TECHNOLOGICAL ADVANCES;

COLOR; COMPUTER AIDED DESIGN; DISEASES; FLUORESCENCE; MEDICAL IMAGING;

COMPUTER AIDED DIAGNOSIS;

EID: 80053028304     PISSN: 10637125     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CBMS.2011.5999110     Document Type: Conference Paper

References (27)

1. L. B. B. Uestuen, W.J. Melssen. Facilitating the application of support vector regression by using a universal Pearson VII function based kernel. Chemometrics and Intelligent Laboratory Systems, 81:29-40, 2006. (Pubitemid 43363446)
2. Bio-Rad Laboratories Inc. PhD System. http://www.biorad. com, 2004.
3. N. Bizzaro, R. Tozzoli, et al. Variability between methods to determine ANA, anti-dsDNA and anti-ENA autoantibodies: a collaborative study with the biomedical industry. Journal of Immunological Methods, 219:99-107, 1998. (Pubitemid 28513155)
4. E. Cordelli and P. Soda. Methods for greyscale representation of hep-2 colour images. In Computer-Based Medical Systems, 23rd IEEE Int. Symp. on, pages 383-388, 12-15 October 2010.
5. Das s.r.l. Service manual AP22 Speedy IF. Palombara Sabina (RI), 5 edition, March 2010.
6. Das s.r.l. SL-IM. Palombara Sabina (RI), 2 edition, 2010.
7. K. Egerer, D. Roggenbuck, et al. Automated evaluation of autoantibodies on human epithelial-2 cells as an approach to standardize cell-based immunofluorescence tests. Arthritis Research & Therapy, 12(40), February 2010.
8. P. Foggia, G. Percannella, et al. Early experiences in mitotic cells recognition on HEp-2 slides. In Computer-Based Medical Systems, 23rd IEEE Int. Symp. on, pages 38-43, 2010.
9. P. Heinlein, J. Drexl, et al. Integrated wavelets for enhancement of microcalcifications in digital mammography. IEEE Trans. on Medical Imaging, 22(3):402-413, 2003.
10. R. Hiemann et al. Challenges of automated screening and differentiation of non-organ specific autoantibodies on HEp- 2 cells. Autoimmunity Reviews, 9(1):17-22, 2009.
11. R. Hiemann, N. Hilger, et al. Objective quality evaluation of fluorescence images to optimize automatic image acquisition. Cytometry Part A, 69:182-184, 2006.
12. Y. L. Huang, Y. L. Jao, et al. Adaptive automatic segmentation of HEp-2 cells in indirect immunofluorescence images. In IEEE Int. Conf. on Sensor Networks, Ubiquitous and Trustworthy Computing, pages 418-422, 2008.
13. ImageInterpret Ltd. Hep-2 pad. Leipzig, Germany, 2009.
14. A. Khotanzad and Y. H. Hong. Invariant image recognition by zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(5):489-497, May 1990.
15. A. D. Kulkarni and P. Byars. Artificial neural network models for texture classification via the radon transform. In Proceedings of the 1992 ACM/SIGAPP symposium on Applied computing, pages 659-664, New York, USA, 1992.
16. Medipan GmbH. Aklides. Dahlewitz, Germany, 2009.
17. Y. Nayatani. Simple estimation methods for the Helmholtz- Kohlrausch effect. Color Research & Application, 22(6):385-401, 1997. (Pubitemid 127570946)
18. P. Soda and G. Iannello. Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies analysis. IEEE Transactions on Information Technology in Biomedicine, 13(3):322-329, 2009.
19. P. Soda, G. Iannello, and M. Vento. A multiple experts system for classifying fluorescence intensity in antinuclear autoantibodies analysis. Pattern Analysis & Applications, 12(3):215-226, September 2009.
20. P. Perner, H. Perner, and B. Muller. Mining knowledge for HEp-2 cell image classification. Journal Artificial Intelligence in Medicine, 26:161-173, 2002. (Pubitemid 35291832)
21. R. C. Gonzalez and R. E. Woods. Digital image processing. Prentice Hall Upper Saddle River, NJ, 2004.
22. A. Rigon et al. Indirect immunofluorescence in autoimmune diseases: Assessment of digital images for diagnostic purpose. Cytometry B (Clinical Cytometry), 72:472-477, 2007. (Pubitemid 350086254)
23. U. Sack, S. Knoechner, et al. Computer-assisted classification of HEp-2 immunofluorescence patterns in autoimmune diagnostics. Autoimmunity Reviews, 2:298-304, 2003. (Pubitemid 37083498)
24. P. Soda, L. Onofri, et al. A decision support system for Crithidia luciliae image classification. Artificial Intelligence in Medicine, 51(1):67-74, 2011.
25. P. Soda, A. Rigon, et al. Automatic acquisition of immunofluorescence images: algorithms and evaluation. In Computer-Based Medical Systems, 2006. 19th IEEE Int. Symp. on, pages 386-390, 2006.
26. T. Ojala, M. Pietikäinen, and T. Mäenpää. Multiresolution gray-scale and rotation invariant texture classification with local binary pattern. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971-987, 2002. (Pubitemid 34835471)
27. R. Tozzoli, N. Bizzaro, et al. Guidelines for the laboratory use of autoantibody tests in the diagnosis and monitoring of autoimmune rheumatic diseases. American Journal of Clinical Pathology, 117(2):316-324, 2002. (Pubitemid 34823674)