Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Pattern Recognition

Volumn 47, Issue 7, 2014, Pages 2325-2337

  Yang, Yan ^a,b   Wiliem, Arnold ^a   Alavi, Azadeh ^a,b   Lovell, Brian C ^a   Hobson, Peter ^c  

^a UNIVERSITY OF QUEENSLAND   (Australia)

^b CSIRO   (Australia)

^c SULLIVAN NICOLAIDES PATHOLOGY   (Australia)

Author keywords

Anti nuclear antibody test; Biologically inspired computer vision; Computer aided diagnostic; HEp 2 cells classification; Independent component analysis; Indirect immunofluorescence

Indexed keywords

BRAIN; CELLS; CLASSIFICATION (OF INFORMATION); COLLAGEN; COMPUTER AIDED ANALYSIS; COMPUTER AIDED DIAGNOSIS; COMPUTER VISION; CYTOLOGY; FLUORESCENCE; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE ENHANCEMENT; MAMMALS; MUSCULOSKELETAL SYSTEM;

ACTIVITY PATTERNS; ANTI-NUCLEAR ANTIBODY TEST; BIOLOGICALLY-INSPIRED COMPUTER VISION; CELL CLASSIFICATION; CELL IMAGES; COMPUTER AIDED DIAGNOSTICS; HUMAN EPITHELIAL TYPE 2 CELL CLASSIFICATION; INDEPENDENT COMPONENTS ANALYSIS; INDIRECT IMMUNOFLUORESCENCE; SPONTANEOUS ACTIVITY;

ANTIBODIES;

EID: 84897115388     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2013.10.013     Document Type: Article

References (55)

1. E. Cordelli, P. Soda, Color to grayscale staining pattern representation in IIF, in: International Symposium on Computer-Based Medical Systems (CBMS), 2011, pp. 1-6.
2. P. Elbischger, S. Geerts, K. Sander, G. Ziervogel-Lukas, P. Sinah, Algorithmic framework for HEp-2 fluorescence pattern classification to aid auto-immune diseases diagnosis, in: Biomedical Imaging: From Nano to Macro, 2009, pp. 562-565.
3. P. Strandmark, J. Ulén, F. Kahl, Hep-2 staining pattern classification, in: International Conference on Pattern Recognition, 2012, pp. 562-565.
4. 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 Autoimmun. Rev. 9 1 2009 17 22
5. T. Hsieh, Y. Huang, C. Chung, Y. Huang, Hep-2 cell classification in indirect immunofluorescence images, in: International Conference on Information, Communications and Signal Processing, 2009.
6. P. Soda, and G. Iannello Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies analysis IEEE Trans. Inf. Technol. Biomed. 13 3 2009 322 329
7. I. Theodorakopoulos, D. Kastaniotis, G. Economou, S. Fotopoulos, Hep-2 cells classification via fusion of morphological and textural features, in: IEEE 12th International Conference on Bioinformatics & Bioengineering, 2012, pp. 689-694.
8. A. Wiliem, Y. Wong, C. Sanderson, P. Hobson, S. Chen, B. Lovell, Classification of human epithelial type 2 cell indirect immunofluoresence images via codebook based descriptors, in: IEEE Workshop on Applications of Computer Vision, 2013.
9. A. Hyvärinen, J. Hurri, and P. Hoyer Natural Image Statistics: A Probabilistic Approach to Early Computational Vision vol. 39 2009 Springer
10. B. Olshausen et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images Nature 381 6583 1996 607 609
11. A. Bell, and T. Sejnowski The 'independent components' of natural scenes are edge filters Vis. Res. 37 23 1997 3327
12. X. Hou, and L. Zhang Dynamic visual attention searching for coding length increments Adv. Neural Inf. Process. Syst. 21 2008 681 688
13. C. Kanan, G. Cottrell, Robust classification of objects, faces, and flowers using natural image statistics, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 2472-2479.
14. Y. Yang, A. Wiliem, A. Alavi, P. Hobson, Classification of human epithelial type 2 cell images using independent component analysis, in: IEEE International Conference on Image Processing, 2013, pp. 733-737.
15. A. Hyvärinen, and E. Oja Independent component analysis algorithms and applications Neural Netw. 13 4 2000 411 430
16. M.V. Albert, A. Schnabel, and D.J. Field Innate visual learning through spontaneous activity patterns PLoS Comput. Biol. 4 8 2008 e1000137
17. D. Stauffer, and A. Aharony Introduction to Percolation Theory 1994 Taylor & Francis
18. J.J. Hunt, M. Ibbotson, and G.J. Goodhill Sparse coding on the spot spontaneous retinal waves suffice for orientation selectivity Neural Comput. 24 9 2012 2422 2433
19. T. Ohshiro, S. Hussain, and M. Weliky Development of cortical orientation selectivity in the absence of visual experience with contour J. Neurophysiol. 106 4 2011 1923 1932
20. X.S. Zhou, T.S. Huang, Image retrieval: feature primitives, feature representation, and relevance feedback, in: IEEE Workshop on Content-based Access of Image and Video Libraries, IEEE, 2000, pp. 10-14.
21. Y. Rui, T.S. Huang, and S.-F. Chang Image retrieval current techniques, promising directions, and open issues J. Vis. Commun. Image Representation 10 1 1999 39 62
22. A.K. Jain, and A. Vailaya Image retrieval using color and shape Pattern Recognition 29 8 1996 1233 1244
23. S. Li, X. Hou, H. Zhang, Q. Cheng, Learning spatially localized, parts-based representation, in: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, 2001, pp. I-207-I-212. http://dx.doi.org/10.1109/CVPR.2001.990477.
24. M.A. Figueiredo, and R.D. Nowak An EM algorithm for wavelet-based image restoration IEEE Trans. Image Process. 12 8 2003 906 916
25. H. Lee, A. Battle, R. Raina, and A.Y. Ng Efficient sparse coding algorithms Adv. Neural Inf. Process. Syst. 19 2007 801
26. H.B. Barlow Pattern recognition and the responses of sensory neurons Ann. N. Y. Acad. Sci. 156 2 1969 872 881
27. J. Ngiam, P. Koh, Z. Chen, S. Bhaskar, and A. Ng Sparse filtering Adv. Neural Inf. Process. Syst. 24 2011 1125 1133
28. H. Lee, R. Grosse, R. Ranganath, A. Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, in: Proceedings of the 26th Annual International Conference on Machine Learning, ACM, 2009, pp. 609-616.
29. D.J. Field What is the goal of sensory coding? Neural Comput. 6 4 1994 559 601
30. S. Hornillo-Mellado, R. Martín-Clemente, J. M. Górriz-Sáez, Connections between ICA and sparse coding revisited, in: Computational Intelligence and Bioinspired Systems, Springer, 2005, pp. 1035-1042.
31. C. Blakemore, R.H. Carpenter, and M.A. Georgeson Lateral inhibition between orientation detectors in the human visual system Nature 228 1970 37 39
32. C. Blakemore, and E.A. Tobin Lateral inhibition between orientation detectors in the cat's visual cortex Exp. Brain Res. 15 4 1972 439 440
33. M.T. Hagan, H.B. Demuth, and M.H. Beale et al. Neural Network Design 1996 PWS Pub. Boston, London
34. A. Hyvärinen The fixed-point algorithm and maximum likelihood estimation for independent component analysis Neural Process. Lett. 10 1 1999 1 5
35. A. Hyvärinen Independent component analysis recent advances Philos. Trans. Roy. Soc. A Math. Phys. Eng. Sci. 371 1984
36. C. Sherrington Observations on the scratch-reflex in the spinal dog J. Physiol. 34 1-2 1906 1
37. C. Sherrington, The Integrative Action of the Nervous System, CUP Archive, 1966.
38. R.W. Rodieck Quantitative analysis of cat retinal ganglion cell response to visual stimuli Vis. Res. 5 12 1965 583 601
39. J.P. Jones, and L.A. Palmer An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex J. Neurophysiol. 58 6 1987 1233 1258
40. S.W. Kuffler et al. Discharge patterns and functional organization of mammalian retina J. Neurophysiol. 16 1 1953 37 68
41. R.C. Reid, R.E. Soodak, and R.M. Shapley Linear mechanisms of directional selectivity in simple cells of cat striate cortex Proc. Natl. Acad. Sci. 84 23 1987 8740 8744
42. E.H. Adelson, and J.R. Bergen Spatiotemporal energy models for the perception of motion J. Opt. Soc. Am. 2 2 1985 284 299
43. P. FGldibk, M.P. Young, Sparse Coding in the Primate Cortex, 1995.
44. J.J. Atick, and A.N. Redlich Towards a theory of early visual processing Neural Comput. 2 3 1990 308 320
45. H. Sherk, and M.P. Stryker Quantitative study of cortical orientation selectivity in visually inexperienced kitten J. Neurophysiol. 39 1 1976 63 70
46. L.E. White, D.M. Coppola, and D. Fitzpatrick The contribution of sensory experience to the maturation of orientation selectivity in ferret visual cortex Nature 411 6841 2001 1049 1052
47. M.P. Stryker, H. Sherk, A.G. Leventhal, and H.V. Hirsch Physiological consequences for the cat's visual cortex of effectively restricting early visual experience with oriented contours J. Neurophysiol. 41 4 1978 896 909
48. S. Tanaka, J. Ribot, K. Imamura, and T. Tani Orientation-restricted continuous visual exposure induces marked reorganization of orientation maps in early life Neuroimage 30 2 2006 462 477
49. B. Chapman, and M.P. Stryker Development of orientation selectivity in ferret visual cortex and effects of deprivation J. Neurosci. 13 12 1993 5251 5262
50. P.-Y. Burgi, and N.M. Grzywacz Model for the pharmacological basis of spontaneous synchronous activity in developing retinas J. Neurosci. 14 12 1994 7426 7439
51. K.B. Godfrey, and N.V. Swindale Retinal wave behavior through activity-dependent refractory periods PLoS Comput. Biol. 3 11 2007 e245
52. S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, in: IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 2169-2178.
53. H. Jiang, and W. Ching Correlation kernels for support vector machines classification with applications in cancer data Comput. Math. Methods Med. 2013 2013 1 6
54. A.S. Wiik, M. Hier-Madsen, J. Forslid, P. Charles, and J. Meyrowitsch Antinuclear antibodies a contemporary nomenclature using HEp-2 cells J. Autoimmun. 35 3 2010 276 290
55. P. Foggia, G. Percannella, P. Soda, and M. Vento Benchmarking HEp-2 cells classification methods IEEE Trans. Med. Imaging 32 10 2013 1878 1889