Abstract
In this paper, we describe a novel image representation strategy for classifying HEp-2 cell patterns of fluorescence staining. Our proposed strategy extends local binary patterns (LBPs), which are state-of-the-art texture features, into local ternary patterns (LTPs) with data-driven thresholds according to Weber’s law, a human perception principle; further, our approach incorporates the contexts of spatial and orientation co-occurrences among adjacent Weber-based local ternary patterns (WLTPs) for texture representation. The explored WLTP is formulated by adaptively quantizing differential values between neighborhood pixels and the focused pixel as negative or positive stimuli if the normalized differential values are large; otherwise the stimulus is set to 0. Our approach here is based on the fact that human perception of a distinguished pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus. By integrating spatial and orientation context information, we further propose a rotation invariant co-occurrence WLTP (RICWLTP) approach to be more discriminant for image representation. Through experiment on the open HEp-2 cell dataset used at the ICIP2013 contest, we confirmed that our proposed strategy can greatly improve recognition performance or achieve comparable performance as compared with state-of-the-art LBP-based descriptor, the conventional LTP, and adaptively codebook/model based methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Conrad, K., Schoessler, W., Hiepe, F., Fritzler, M.J.: Autoantibodies in Systemic Autoimmune Diseases. Pabst Science Publishers, Lengerich (2002)
Soda, P., Iannello, G.: Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies analysis. IEEE Trans. Inf. Technol. Biomed. 13(3), 322–329 (2009)
Cataldo, S.D., Bottino, A., Ficarra E., Macii, E.: Applying textural features to the classification of hep-2 cell patterns in IIF images. In: International Conference of Pattern Recognition (ICPR2012), pp. 28–43 (2012)
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. In: Workshop on Application of Computer Vision, pp. 95–102 (2013)
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(8), 2223–2234 (2014)
Wang, X.Y., Han, T.X., Yan, S.C.: An HOG-LBP human detector with partial occlusion handling, ICCV (2009)
Foggia, P., Percannella, G., Soda, P., Vento, M.: Benchmarking HEp-2 cells classification methods. IEEE Trans. Med. Imag. 32(10), 1878–1889 (2013)
Nosaka, R., Ohkawa, Y., Fukui, K.: Feature extraction based on co-occurrence of adjacent local binary patterns. In: The 5th Pacific-Rim Symposium on Image and Video Technology (PSIVT2011). Part II, LNCS, vol. 7088, pp. 82–91 (2011)
Nosaka, R., Fukui, K.: HEp-2 cell classification using rotation invariant co-occurrence among local binary patterns, Pattern Recognition (2013)
Qi, X.B., Xiao, R., Zhang, L., Guo, J.: Pairwise rotation invariant co-occurrence local binary pattern. In: 12th European Conference on Computer Vision (2012)
Tan, X.Y., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Han, X.H., Chen, Y.W., Xu, G. (2016). Coccurrence Statistics of Local Ternary Patterns for HEp-2 Cell Classification. In: Chen, YW., Torro, C., Tanaka, S., Howlett, R., C. Jain, L. (eds) Innovation in Medicine and Healthcare 2015. Smart Innovation, Systems and Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-23024-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-23024-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23023-8
Online ISBN: 978-3-319-23024-5
eBook Packages: EngineeringEngineering (R0)