Integration of spatial and orientation contexts in local ternary patterns for HEp-2 cell classification

Pattern Recognition Letters

Volumn 82, Issue , 2016, Pages 23-27

  Han, Xian Hua ^a   Chen, Yen Wei ^a   Xu, Gang ^a  

^a RITSUMEIKAN UNIVERSITY   (Japan)

Author keywords

Context integration; Data driven threshold; HEp 2 image classification; Local ternary pattern; Weber's law

Indexed keywords

KNOWLEDGE REPRESENTATION; PIXELS; TEXTURES;

CONTEXT INFORMATION; DATA DRIVEN; FLUORESCENCE STAINING; IMAGE REPRESENTATIONS; LOCAL BINARY PATTERNS; LOCAL TERNARY PATTERNS; TEXTURE REPRESENTATION; WEBER'S LAW;

IMAGE CLASSIFICATION;

EID: 84995559843     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2016.02.004     Document Type: Article

References (17)

1. [1] Cataldo, S., Bottino, A., Ficarra, E., Macii, E., Applying textural features to the classification of HEp-2 cell patterns in IIF images. Proceedings of International Conference of Pattern Recognition (ICPR2012), 2012, 3349–3352.
2. [2] Conrad, K., Schoessler, W., Hiepe, F., Fritzler, M.J., Autoantibodies in Systemic Autoimmune Diseases, 2ed, 2002, Pabst Science Publishers, Lengerich, Germany.
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] Foggia, P., Percannella, G., Soda, P., Vento, M., Benchmarking hep-2 cells classification methods. IEEE Trans. Med. Imaging 32 (2013), 1878–1889.
5. [5] Han, X.-H., Wang, J., Xu, G., Chen, Y.-W., High-order statistics of microtexton for hep-2 staining pattern classification. IEEE Trans. Biomed. Eng. 61 (2014), 2223–2234.
6. [6] Hobson, P., Lovell, B., Percannella, G., Vento, M., Wiliem, A., Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset. Artif. Intell. Med. 65 (2015), 239–250.
7. [7] Larsen, A., Vestergaard, J., Larsen, R., Hep-2 cell classification using shape index histograms with donut-shaped spatial pooling. IEEE Trans. Med. Imaging 33 (2014), 1573–1580.
8. [8] Nosaka, R., Fukui, K., Hep-2 cell classification using rotation invariant co-occurrence among local binary patterns. Pattern Recognit. 47 (2014), 2428–2436.
9. [9] Nosaka, R., Y., O., Fukui, K., Feature extraction based on co-occurrence of adjacent local binary patterns. The 5th Pacific-Rim Symposium on Image and Video Technology (PSIVT2011), vol. 2, 2011, 82–91.
10. [10] Ojala, T., Pietikainen, M., Maenpaa, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI 25 (2002), 971–987.
11. [11] Qi, X., Xiao, R., J., G., Zhang, L., Pairwise rotation invariant co-occurrence local binary pattern. Proceedings of the 12th European Conference on Computer Vision (ECCV2012), LNCS 7577, 2012, 158–171.
12. [12] Qi, X., Xiao, R., Zhang, L., Guo, J., Pairwise rotation invariant co-occurrence local binary pattern. IEEE Trans. Pattern Anal. Mach. Intell. 36 (2014), 2199–2213.
13. [13] Soda, P., Iannello, G., Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies analysis. IEEE Trans. Inf. Technol. Biomed. 13 (2009), 322–329.
14. [14] Tan, X.Y., Triggs, B., Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19 (2010), 1635–1650.
15. [15] Vecsei, A., Amann, G., Hegenbart, S., Liedlgruber, M., Uhl, A., Automated marsh-like classification of celiac disease in children using local texture operators. Comput. Bio. Med. 41 (2012), 313–325.
16. [16] Wang, X., Han, T.X., Yan, S., An hog-lbp human detector with partial occlusion handling. ICCV, 2009, 32–39.
17. [17] Wiliem, A., Wong, Y., Sanderson, C., Hobson, P., Chen, S., Lovell, B.C, Classification of human epithetial type 2 cell indirect immunofluorescence images via codebook based descriptors. Workshop on Application of Computer Vision, 2013, 95–102.