Efficient k-NN based HEp-2 cells classifier

Pattern Recognition

Volumn 47, Issue 7, 2014, Pages 2409-2418

  Stoklasa, Roman ^a   Majtner, Tomáš ^a   Svoboda, David ^a  

^a MASARYK UNIVERSITY   (Czech Republic)

Author keywords

Classification; Classifier; HEp 2 cells; IIF; Image descriptor; Indirect Immunofluorescence; Nearest neighbours

Indexed keywords

COMPUTER AIDED ANALYSIS; COMPUTER AIDED DIAGNOSIS; CYTOLOGY; FLUORESCENCE; IMAGE CLASSIFICATION; LOCAL BINARY PATTERN; NEAREST NEIGHBOR SEARCH; VECTOR SPACES;

CELL-BE; CELL/B.E; CELL/BE; DESCRIPTORS; HEP-2 CELL; IMAGE DESCRIPTOR; INDIRECT IMMUNO-FLUORESCENCE TEST; INDIRECT IMMUNOFLUORESCENCE; NEAREST-NEIGHBOUR;

CELLS;

EID: 84897111964     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2013.09.021     Document Type: Article

References (59)

1. P. Foggia, G. Percannella, P. Soda, and M. Vento Benchmarking HEp-2 cells classification methods IEEE Transactions on Medical Imaging PP 99 2013 1878 1889
2. R. Tozzoli, N. Bizzaro, E. Tonutti, D. Villalta, D. Bassetti, F. Manoni, A. Piazza, M. Pradella, and P. Rizzotti Guidelines for the laboratory use of autoantibody tests in the diagnosis and monitoring of autoimmune rheumatic diseases American Journal of Clinical Pathology 117 2 2002 316 324
3. D.H. Solomon, A.J. Kavanaugh, and P.H. Schur American, evidence-based guidelines for the use of immunologic tests antinuclear antibody testing Arthritis & Rheumatism 47 4 2002 434 444
4. P. Foggia, G. Percannella, P. Soda, M. Vento, Early experiences in mitotic cells recognition on HEp-2 slides, in: 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS), 2010, pp. 38-43.
5. A. Bradwell, R. Hughes, E. Harden, Atlas of Hep-2 Patterns: & Laboratory Techniques, Binding Site, 2003.
6. E. Cordelli, P. Soda, Color to grayscale staining pattern representation in IIF, in: 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), 2011, pp. 1-6.
7. BioSystems S.A., Costa Brava 30, Barcelona, Spain, Anti-Nuclear Antibodies HEp-2 (ANA-HEp-2), URL 〈 http://www.biosystems.es/corpdocs/ downer.aspx?arxiu=44508i.PDF&tipus=1 〉.
8. G. Percannella, P. Soda, M. Vento, A classification-based approach to segment HEp-2 cells, in: 2012 25th International Symposium on Computer-Based Medical Systems (CBMS), 2012, pp. 1-5.
9. P. Soda, A. Rigon, A. Afeltra, G. Iannello, Automatic acquisition of immunofluorescence images: algorithms and evaluation, in: The 19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006, 2006, pp. 386-390.
10. R. Hiemann, N. Hilger, U. Sack, and M. Weigert Objective quality evaluation of fluorescence images to optimize automatic image acquisition Cytometry Part A 69A 3 2006 182 184
11. Y.-L. Huang, C.-W. Chung, T.-Y. Hsieh, Y.-L. Jao, Outline detection for the HEp-2 cell in indirect immunofluorescence images using watershed segmentation, in: IEEE International Conference on Sensor Networks, Ubiquitous and Trustworthy Computing, SUTC '08, 2008, pp. 423-427.
12. C. Creemers, K. Guerti, S. Geerts, K. Van Cotthem, A. Ledda, V. Spruyt, Hep-2 cell pattern segmentation for the support of autoimmune disease diagnosis, in: Proceedings of the Fourth International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL '11, ACM, New York, NY, USA, 2011, pp. 1-5.
13. R. Hiemann, N. Hilger, J. Michel, J. Nitschke, A. Böhm, U. Anderer, M. Weigert, and U. Sack Automatic analysis of immunofluorescence patterns of HEp-2 cells Annals of the New York Academy of Sciences 1109 1 2007 358 371
14. 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 2009 322 329
15. P. Perner, H. Perner, and B. Müller Mining knowledge for HEp-2 cell image classification Artificial Intelligence in Medicine 26 2002 161 173
16. U. Sack, S. Knoechner, H. Warschkau, U. Pigla, F. Emmrich, and M. Kamprad Computer-assisted classification of HEp-2 immunofluorescence patterns in autoimmune diagnostics Autoimmunity Reviews 2 5 2003 298 304
17. K. Li, J. Yin, Z. Lu, X. Kong, R. Zhang, W. Liu, Multiclass boosting svm using different texture features in HEp-2 cell staining pattern classification, in: 2012 IEEE 21st International Conference on Pattern Recognition (ICPR), 2012, pp. 170-173.
18. S. Ghosh, V. Chaudhary, Feature analysis for automatic classification of HEp-2 florescence patterns: Computer-aided diagnosis of auto-immune diseases, in: 2012 IEEE 21st International Conference on Pattern Recognition (ICPR), 2012, pp. 174-177.
19. S. Di Cataldo, A. Bottino, E. Ficarra, E. Macii, Applying textural features to the classification of HEp-2 cell patterns in IIF images, in: 2012 IEEE 21st International Conference on Pattern Recognition (ICPR), 2012, pp. 3349-3352.
20. I. Ersoy, F. Bunyak, J. Peng, K. Palaniappan, Hep-2 cell classification in IIF images using shareboost, in: 2012 IEEE 21st International Conference on Pattern Recognition (ICPR), 2012, pp. 3362-3365.
21. P. Strandmark, J. Ulen, F. Kahl, HEp-2 staining pattern classification, in: 2012 21st International Conference on Pattern Recognition (ICPR), 2012, pp. 33-36.
22. V. Snell, W. Christmas, J. Kittler, Texture and shape in fluorescence pattern identification for auto-immune disease diagnosis, in: 2012 IEEE 21st International Conference on Pattern Recognition (ICPR), 2012, pp. 3750-3753.
23. A. Wiliem, Y. Wong, C. Sanderson, P. Hobson, S. Chen, B. Lovell, Classification of human epithelial type 2 cell indirect immunofluorescence images via codebook based descriptors, in: IEEE Workshop on Application of Computer Vision (WACV), 2013.
24. R. Hiemann, T. Büttner, T. Krieger, D. Roggenbuck, U. Sack, and K. Conrad Challenges of automated screening and differentiation of non-organ specific autoantibodies on HEp-2 cells Autoimmunity Reviews 9 1 2009 17 22
25. N. Bizzaro, R. Tozzoli, E. Tonutti, A. Piazza, F. Manoni, A. Ghirardello, D. Bassetti, D. Villalta, M. Pradella, and P. Rizzotti Variability between methods to determine ANA, anti-dsDNA and anti-ENA autoantibodies a collaborative study with the biomedical industry Journal of Immunological Methods 219 1 1998 99 107
26. M. Sonka, V. Hlavac, and R. Boyle Image Processing, Analysis, and Machine Vision 2007 Thomson-Engineering
27. J.B. MacQueen Some methods for classification and analysis of multivariate observations L.M.L. Cam, J. Neyman, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability vol. 1 1967 University of California Press 281 297
28. V.N. Vapnik An overview of statistical learning theory IEEE Transactions on Neural Networks 10 5 1999 988 999
29. A. Mathur, and G. Foody Multiclass and binary SVM classification implications for training and classification users IEEE Geoscience and Remote Sensing Letters 5 2 2008 241 245
30. M.V. Boland, M.K. Markey, and R.F. Murphy Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images Cytometry 33 3 1998 366 375
31. C.-F. Tsai, K. McGarry, J. Tait, Image classification using hybrid neural networks, in: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '03, ACM, New York, NY, USA, 2003, pp. 431-432.
32. J.R. Quinlan Induction of decision trees Machine Learning 1 1 1986 81 106
33. A. Bosch, A. Zisserman, X. Muoz, Image classification using random forests and ferns, in: IEEE 11th International Conference on Computer Vision, ICCV 2007, 2007, pp. 1-8.
34. E. Fix, Jr., Discriminatory Analysis: Nonparametric Discrimination: Consistency Properties, Technical Report Project 21-49-004, Report Number 4, USAF School of Aviation Medicine, Randolf Field, Texas, 1951.
35. S.A. Dudani The distance-weighted k-nearest-neighbor rule IEEE Transactions on Systems, Man and Cybernetics 6 4 1976 325 327
36. R.J. Samworth Optimal weighted nearest neighbour classifiers Annals of Statistics 40 5 2012 2733 2763
37. O. Boiman, E. Shechtman, M. Irani, In defense of nearest-neighbor based image classification, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, 2008, pp. 1-8.
38. M. Batko, D. Novak, P. Zezula, MESSIF: metric similarity search implementation framework, in: DELOS'07, URL 〈 http://lsd.fi.muni.cz/trac/ messif/ 〉, 2007.
39. R.M. Haralick, K. Shanmugam, and I. Dinstein Textural features for image classification IEEE Transactions on Systems, Man and Cybernetics 3 6 1973 610 621
40. M.-H. Horng, Y.-N. Sun, and X.-Z. Lin Texture feature coding method for classification of liver sonography Computerized Medical Imaging and Graphics 26 1 2002 33 42
41. M.V. Boland, and R.F. Murphy A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells Bioinformatics 17 12 2001 1213 1223
42. R.F. Murphy, M. Velliste, J. Yao, G. Porreca, Searching online journals for fluorescence microscope images depicting protein subcellular location patterns, in: Proceedings of the Second IEEE International Symposium on Bioinformatics and Bioengineering, BIBE '01, IEEE Computer Society, Washington, DC, USA, 2001, pp. 119-128.
43. R. Murphy, M. Velliste, G. Porreca, Robust classification of subcellular location patterns in fluorescence microscope images, in: Proceedings of the 2002 12th IEEE Workshop on Neural Networks for Signal Processing, 2002, pp. 67-76.
44. T. Ojala, M. Pietikainen, D. Harwood, Performance evaluation of texture measures with classification based on Kullback discrimination of distributions, in: Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994, vol. 1 - Conference A: Computer Vision & Image Processing, 1994, pp. 582-585.
45. L. Wang, and D.-C. He Texture classification using texture spectrum Pattern Recognition 23 8 1990 905 910
46. T. Ojala, M. Pietikainen, and T. Maenpaa Multiresolution gray-scale and rotation invariant texture classification with local binary patterns IEEE Transactions on Pattern Analysis and Machine Intelligence 24 7 2002 971 987
47. M. Pietikainen, T. Nurmela, T. Maenpaa, M. Turtinen, View-based recognition of 3d-textured surfaces, in: The 12th International Conference on Image Analysis and Processing, 2003, pp. 530-535.
48. B. Manjunath, J.-R. Ohm, V. Vasudevan, and A. Yamada Color and texture descriptors IEEE Transactions on Circuits and Systems for Video Technology 11 6 2001 703 715
49. P. Salembier, and T. Sikora Introduction to MPEG-7 Multimedia Content Description Interface 2002 John Wiley & Sons, Inc. New York, NY, USA
50. R. Qian, P. Van Beek, M. Sezan, Image retrieval using blob histograms, in: 2000 IEEE International Conference on Multimedia and Expo, ICME 2000, vol. 1, 2000, pp. 125-128.
51. D.S. Messing, P. Beek, J.H. Errico, The MPEG-7 colour structure descriptor: image description using color and local spatial information, in: Proceedings of the International Conference on Image Processing, 2001, pp. 670-673.
52. J.-R. Ohm, L. Cieplinski, H.J. Kim, S. Krishnamachari, B. Manjunath, D.S. Messing, A. Yamada, The MPEG-7 color descriptors, in: Introduction to MPEG-7: Multimedia Content Description Interface, Wiley, 2001.
53. D. Lowe, Object recognition from local scale-invariant features, in: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, 1999, pp. 1150-1157.
54. D.G. Lowe Distinctive image features from scale-invariant keypoints International Journal of Computer Vision 60 2 2004 91 110
55. J. Matas, O. Chum, M. Urban, and T. Pajdla Robust wide-baseline stereo from maximally stable extremal regions Image and Vision Computing 22 10 2004 761 767
56. J. Serra Image Analysis and Mathematical Morphology 1983 Academic Press, Inc. Orlando, FL, USA
57. T. Damoulas, and M.A. Girolami Combining feature spaces for classification Pattern Recognition 42 11 2009 2671 2683
58. J. Kittler, M. Hatef, R.P. Duin, and J. Matas On combining classifiers IEEE Transactions on Pattern Analysis and Machine Intelligence 20 3 1998 226 239
59. D.M. Tax, M. Van Breukelen, R.P. Duin, and J. Kittler Combining multiple classifiers by averaging or by multiplying? Pattern Recognition 33 9 2000 1475 1485