Adaptive Co-occurrence Differential Texton space for HEp-2 cells classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9351, Issue , 2015, Pages 260-267

  Xu, Xiang ^a   Lin, Feng ^a   Ng, Carol ^b   Leong, Khai Pang ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b TAN TOCK SENG HOSPITAL   (Singapore)

Author keywords

Adaptive partitioned feature space; Co occurrence differential texton; Generative model; HEp 2 cells

Indexed keywords

CELLS; CYTOLOGY; MEDICAL COMPUTING; MEDICAL IMAGING; VECTOR SPACES;

ANTINUCLEAR ANTIBODY; AUTOMATIC CLASSIFICATION SYSTEMS; CO-OCCURRENCE; FEATURE SPACE; GENERATIVE MODEL; HEP-2 CELLS; HUMAN SUBJECTIVE EVALUATION; LINEAR SUPPORT VECTOR MACHINES;

CLASSIFICATION (OF INFORMATION);

EID: 84951818129     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-24574-4_31     Document Type: Conference Paper

References (13)

1. Ryusuke, N., Kazuhiro, F.: HEp-2 cell classification using rotation invariant co-occurrence among local binary patterns. Pattern Recogn. 47(7), 2428-2436 (2014)
2. Foggia, P., Percannella, G., Soda, P., Vento, M.: Benchmarking HEp-2 cells classification methods. IEEE Trans. Med. Imaging 32(10), 1878-1889 (2013)
3. Xu, X., Lin, F., Carol, Ng, Leong, K.P.: Linear local distance coding for classification of HEp-2 staining patterns. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 393-400 (2014)
4. Shen, L.L., Lin, J.M., Wu, S.Y., Yu, S.Q.: HEp-2 image classification using intensity order pooling based features and bag of words. Pattern Recogn. 47(7), 2419-2427 (2014)
5. Theodorakopoulos, I., Kastaniotis, D., Economou, G., Fotopoulos, S.: Hep-2 cells classification via sparse representation of textural features fused into dissimilarity space. Pattern Recogn. 47(7), 2367-2378 (2014)
6. Xu, X., Lin, F., Carol, Ng, Leong, K.P.: Staining Pattern Classification of ANA-IIF Based on SIFT Features. J. Medical Imag. Heal. Inform. (2)4, 419-424 (2012)
7. Nanni, L., Paci, M., Brahnam, S.: Indirect immunofluorescence image classification using texture descriptors. Expert Syst. Appl. 41(5), 2463-2471 (2014)
8. Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8 (2007)
9. Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8 (2008)
10. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. 24(7), 971-987 (2002)
11. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Series B (Methodological) 1-38 (1977)
12. Sánchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the Fisher vector: Theory and practice. Int. J. Comput. Vision 105(3), 222-245 (2013)
13. Liu, L.Q., Wang, L., Liu, X.W.: In defense of soft-assignment coding. In: IEEE International Conference on Computer Vision (ICCV), pp. 2486-2493 (2011)