Analyzing features by SWLDA for the classification of HEp-2 cell images using GMM

Pattern Recognition Letters

Volumn 82, Issue , 2016, Pages 44-55

  Sarrafzadeh, Omid ^a,b   Rabbani, Hossein ^a,b   Mehri Dehnavi, Alireza ^a,b   Talebi, Ardeshir ^c  

^a ISFAHAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^b ISFAHAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^c ISFAHAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

Author keywords

Automatic system; Gaussian Mixture Model; Indirect immunofluorescence; Step Wise Linear Discriminant Analysis

Indexed keywords

DISCRIMINANT ANALYSIS; GAUSSIAN DISTRIBUTION; WAVELET ANALYSIS;

AUTOMATIC CLASSIFICATION; AUTOMATIC SYSTEMS; GAUSSIAN MIXTURE MODEL; HUMAN EPITHELIAL CELLS; INDIRECT IMMUNOFLUORESCENCE; LINEAR DISCRIMINANT ANALYSIS; NORMALIZED IMAGE; STANDARD METRICS;

FLUORESCENCE;

EID: 84975455082     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2016.03.023     Document Type: Article

References (31)

1. [1] Foggia, P., Percannella, G., Soda, P., Vento, M., Benchmarking HEp-2 cells classification methods. IEEE Trans. Med. Imaging 32 (2013), 1878–1889.
2. [2] Hobson, P., Lovell, B.C., Percannella, G., Vento, M., Wiliem, A., Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset. Artif. Intell. Med. 65:3 (2015), 239–250.
3. [3] Foggia, P., Percannella, G., Saggese, A., Vento, M., Pattern recognition in stained hep-2 cells: where are we now?. Pattern Recognit. 47 (2014), 2305–2314.
4. [4] Shen, L., Lin, J., Wu, S., Yu, S., HEp-2 image classification using intensity order pooling based features and bag of words. Pattern Recognit. 47 (2014), 2419–2427.
5. [5] Ensafi, S., Lu, S., Kassim, A.A., Tan, C.L., A Bag of Words Based Approach for Classification of HEp-2 Cell Images. Proceedings of the 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images (I3A), 2014, 29–32.
6. [6] Zhao, Y., Gao, Z., Wang, L., Zhou, L., Experimental Study of Unsupervised Feature Learning for HEp-2 Cell Images Clustering. Proceedings of the 2014 International Conference on Digital lmage Computing: Techniques and Applications (DlCTA), 2014, 1–8.
7. [7] Liu, L., Wang, L., HEp-2 cell image classification with multiple linear descriptors. Pattern Recognit. 47 (2014), 2400–2408.
8. [8] Faraki, M., Harandi, M.T., Wiliem, A., Lovell, B.C., Fisher tensors for classifying human epithelial cells. Pattern Recognit. 47 (2014), 2348–2359.
9. [9] Wiliem, A., Sanderson, C., Wong, Y., Hobson, P., Minchin, R.F., Lovell, B.C., Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching. Pattern Recognit. 47 (2014), 2315–2324.
10. [10] Manivannan, S., Li, W., Akbar, S., Wang, R., Zhang, J., McKenna, S.J., Hep-2 cell classification using multi-resolution local patterns and ensemble SVMs. Proceedings of the 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images (I3A), 2014, 37–40.
11. [11] Kong, X., Li, K., Cao, J., Yang, Q., Wenyin, L., Hep-2 cell pattern classification with discriminative dictionary learning. Pattern Recognition 47 (2014), 2379–2388.
12. [12] Theodorakopoulos, I., Kastaniotis, D., Economou, G., Fotopoulos, S., Hep-2 cells classification via sparse representation of textural features fused into dissimilarity space. Pattern Recognit. 47 (2014), 2367–2378.
13. [13] Ponomarev, G.V., Arlazarov, V.L., Gelfand, M.S., Kazanov, M.D., ANA HEp-2 cells image classification using number, size, shape and localization of targeted cell regions. Pattern Recognit. 47 (2014), 2360–2366.
14. [14] Sriram, A., Ensafi, S., Roohi, S.F., Kassim, A.A., Classification of human epithelial type-2 cells using hierarchical segregation. Proceedings of the 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014, 323–328.
15. [15] Stoklasa, R., Majtner, T., Svoboda, D., Efficient k-NN based HEp-2 cells classifier. Pattern Recognit. 47 (2014), 2409–2418.
16. [16] Larsen, A.B.L., Vestergaard, J.S., Larsen, R., HEp-2 cell classification using shape index histograms with donut-shaped spatial pooling, Medical Imaging. IEEE Transaction on 33:7 (2014), 1573–1580.
17. [17] Nosaka, R., Fukui, K., Hep-2 cell classification using rotation invariant co-occurrence among local binary patterns. Pattern Recognit. 47 (2014), 2428–2436.
18. [18] Ojala, T., Pietikäinen, M., Mäenpää, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002), 971–987.
19. [19] Gonzalez, R.C., Woods, R.E., Eddins, S.L., Digital Image Processing Using MATLAB. 2004, Pearson Education India.
20. [20] Haralick, R.M., Shanmugam, K., Dinstein, I.H., Textural features for image classification., 1973, 610–621.
21. [21] Guo, Z., Zhang, D., A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19 (2010), 1657–1663.
22. [22] Sonka, M., Hlavac, V., Boyle, R., Image Processing, Analysis, and Machine Vision. 2014, Cengage Learning.
23. [23] Multilevel 2-D wavelet decomposition. Available: http://www.mathworks.com/help/wavelet/ref/wavedec2.html. (accessed 08.05.16).
24. [24] Wavelet filters. Available: http://www.mathworks.com/help/wavelet/ref/wfilters.html. (accessed 08.05.16).
25. [25] Haghighat, M., Zonouz, S., Abdel-Mottaleb, M., Identification using encrypted biometrics., 2013, 440–448.
26. [26] M.H. Kutner, C.J. Nachtsheim, J. Neter, W. Li, Applied linear statistical models, McGraw-Hill, Irwin, 5e 2005.
27. [27] IBM SPSS, IBM SPSS Statistics Base 19, 2010.
28. [28] Dempster, A.P., Laird, N.M., Rubin, D.B., Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B: Methodol., 1977, 1–38.
29. [29] McLachlan, G., Peel, D., Finite Mixture Models. 2004, John Wiley & Sons.
30. [30] Theodorakopoulos, I., Kastaniotis, D., Economou, G., Fotopoulos, S., HEp-2 cells classification using morphological features and a bundle of local gradient descriptors. Proceedings of the 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images (I3A), 2014, 33–36.
31. [31] Gragnaniello, D., Sansone, C., Verdoliva, L., Biologically-inspired dense local descriptor for indirect immunofluorescence image classification. Proceedings of the 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images (I3A), 2014, 1–5.