Recurrence quantification analysis statistics for image feature extraction and classification

Abstract

Advances in computer vision technology have expanded the possibilities to facilitate complex task automation for integration into large-scale data processing solutions. Despite these advances, however, there is still a need to develop simple and efficient algorithms for image feature extraction and classification to enable easier and faster implementation into real-world applications. Here, a new method is described to extract features from images that can be used for image classification. It uses a fuzzy c-means (FCM) clustering-based approach that allows for unique object patterns to be spatially re-mapped onto a binary sparse matrix with which principles from recurrence quantification analysis statistics (RQAS) can be applied. RQAS are computationally efficient and can be used to create a short feature vector for effective binary and multi-class image classification. The utility of this method is demonstrated using both simulated and real datasets that include objects embedded in complex backgrounds, and is compared with another widely used and highly effective thresholding feature extraction method (local binary patterns (LBP)). Results show that the FCM-RQAS method described here can perform as well or better than LBP and supports the use and further development of RQAS-based image feature extraction for computer vision applications.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Data availability

The datasets analyzed in the current study are already available as cited in the text and can also be made available from the corresponding author on reasonable request.

References

  1. 1.

    J. Gao, Y. Yang, P. Lin, D.S. Park, Computer vision in healthcare applications. J Healthc Eng 2018, 5157020 (2018). https://doi.org/10.1155/2018/5157020

    Article  Google Scholar 

  2. 2.

    A. Nasirahmadi, B. Sturm, A.-C. Olsson, K.-H. Jeppsson, S. Müller, S. Edwards, O. Hensel, Automatic scoring of lateral and sternal lying posture in grouped pigs using image processing and support vector machine. Comput Electron Agric 156, 475–481 (2019). https://doi.org/10.1016/J.COMPAG.2018.12.009

    Article  Google Scholar 

  3. 3.

    K. Chui, W. Alhalabi, S. Pang, P. Pablos, R. Liu, M. Zhao, K.T. Chui, W. Alhalabi, S.S.H. Pang, P.O. de Pablos, R.W. Liu, M. Zhao, Disease diagnosis in smart healthcare: Innovation, technologies and applications. Sustainability. 9, 2309 (2017). https://doi.org/10.3390/su9122309

    Article  Google Scholar 

  4. 4.

    A. Gudigar, U. Raghavendra, T. Devasia, K. Nayak, S.M. Danish, G. Kamath, J. Samanth, U.M. Pai, V. Nayak, R.S. Tan, E.J. Ciaccio, U.R. Acharya, Global weighted LBP based entropy features for the assessment of pulmonary hypertension. Pattern Recogn Lett 125, 35–41 (2019). https://doi.org/10.1016/J.PATREC.2019.03.027

    Article  Google Scholar 

  5. 5.

    A. Bakhshipour, A. Jafari, Evaluation of support vector machine and artificial neural networks in weed detection using shape features. Comput Electron Agric 145, 153–160 (2018). https://doi.org/10.1016/J.COMPAG.2017.12.032

    Article  Google Scholar 

  6. 6.

    D.S. Jodas, N. Marranghello, A.S. Pereira, R.C. Guido, Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of Mobile robots through paths in plantations. Procedia Comput Sci 18, 240–249 (2013). https://doi.org/10.1016/J.PROCS.2013.05.187

    Article  Google Scholar 

  7. 7.

    G. Sakr, M. Mokbel, … A A.D.-M. U 2016, Comparing deep learning and support vector machines for autonomous waste sorting, 2016 IEEE Int Multidiscip Conf Eng Technol 207–212 (2016).

  8. 8.

    M.-E. Nilsback, A. Zisserman, Delving deeper into the whorl of flower segmentation. Image Vis Comput 28, 1049–1062 (2010). https://doi.org/10.1016/J.IMAVIS.2009.10.001

    Article  Google Scholar 

  9. 9.

    Y.-K. Chan, M.-H. Tsai, D.-C. Huang, Z.-H. Zheng, K.-D. Hung, Leukocyte nucleus segmentation and nucleus lobe counting. BMC Bioinformatics 11, 558 (2010). https://doi.org/10.1186/1471-2105-11-558

    Article  Google Scholar 

  10. 10.

    S. Han, E. Taralova, C. Dupre, R. Yuste, Comprehensive machine learning analysis of Hydra behavior reveals a stable basal behavioral repertoire. Elife 7, e32605 (2018). https://doi.org/10.7554/eLife.32605

  11. 11.

    P. Kumar, D.K. Gupta, V.N. Mishra, R. Prasad, Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data. Int J Remote Sens 36, 1604–1617 (2015). https://doi.org/10.1080/2150704X.2015.1019015

    Article  Google Scholar 

  12. 12.

    M. Gamarra, E. Zurek, H. San-Juan, Study of image analysis algorithms for segmentation, feature extraction and classification of cells. J Inf Syst Eng Manag 2, 20 (2017). https://doi.org/10.20897/jisem.201720

    Article  Google Scholar 

  13. 13.

    M.S. Fasihi, W.B. Mikhael, Overview of Current Biomedical Image Segmentation Methods. 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, 2016, pp. 803–808

  14. 14.

    A. Khan, S. Ravi, Image segmentation methods: A comparative study. Int J Soft Comput Eng 3, 84–92 (2013)

    Google Scholar 

  15. 15.

    G. Kumar, P.K. Bhatia, A detailed review of feature extraction in image processing systems. 2014 Fourth Int. Conf. Adv. Comput. Commun. Technol.Rohtak (2014), pp. 5–12. https://doi.org/10.1109/ACCT.2014.74

  16. 16.

    Y. Yu, K. Zhang, L. Yang, D. Zhang, Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN. Comput Electron Agric 163, 104846 (2019). https://doi.org/10.1016/J.COMPAG.2019.06.001

    Article  Google Scholar 

  17. 17.

    W. Wang, Y. Zhang, On fuzzy cluster validity indices. Fuzzy Sets Syst 158, 2095–2117 (2007). https://doi.org/10.1016/J.FSS.2007.03.004

    MathSciNet  Article  MATH  Google Scholar 

  18. 18.

    M. Yambal, H. Gupta, Image segmentation using fuzzy C means clustering: A survey. Int J Adv Res Comput Commun Eng 2, 2927–2929 (2013)

    Google Scholar 

  19. 19.

    S. Naz, H. Majeed, H. Irshad, Image segmentation using fuzzy clustering: A survey. 2010 6th Int. Conf. Emerg. Technol. ICET), Islamabad (2010), pp. 181–186. https://doi.org/10.1109/ICET.2010.5638492

  20. 20.

    K.-L. Wu, Analysis of parameter selections for fuzzy c-means. Pattern Recogn 45, 407–415 (2012). https://doi.org/10.1016/J.PATCOG.2011.07.012

    Article  MATH  Google Scholar 

  21. 21.

    J. Schulz, A. Mentges, O. Zielinski, Deriving image features for autonomous classification from time-series recurrence plots. J Eur Opt Soc Publ 12, 5 (2016). https://doi.org/10.1186/s41476-016-0003-y

    Article  Google Scholar 

  22. 22.

    T. Chomiak, W. Xian, Z. Pei, B. Hu, A novel single-sensor-based method for the detection of gait-cycle breakdown and freezing of gait in Parkinson’s disease. J Neural Transm 126, 1029–1036 (2019). https://doi.org/10.1007/s00702-019-02020-0

    Article  Google Scholar 

  23. 23.

    W.J. Bosl, H. Tager-Flusberg, C.A. Nelson, EEG analytics for early detection of autism spectrum disorder: A data-driven approach. Sci Rep 8, 6828 (2018). https://doi.org/10.1038/s41598-018-24318-x

    Article  Google Scholar 

  24. 24.

    C.L. Webber, J.P. Zbilut, in Recurrence quantification analysis of nonlinear dynamical systems, ed. by M. Riley, G. Van Orden. (National Science Foundation, Arlington, VA, 2005), pp. 26–95

    Google Scholar 

  25. 25.

    O. Afsar, U. Tirnakli, N. Marwan, Recurrence quantification analysis at work: Quasi-periodicity based interpretation of gait force profiles for patients with Parkinson disease. Sci Rep 8, 9102 (2018). https://doi.org/10.1038/s41598-018-27369-2

    Article  Google Scholar 

  26. 26.

    N. Marwan, M. Romano, M. Thiel, J. Kurths, Recurrence plots for the analysis of complex systems. Phys Rep 438, 237–329 (2007). https://doi.org/10.1016/j.physrep.2006.11.001

    MathSciNet  Article  Google Scholar 

  27. 27.

    S. Wallot, A. Roepstorff, D. Mønster, Multidimensional recurrence quantification analysis (MdRQA) for the analysis of multidimensional time-series: A software implementation in MATLAB and its application to group-level data in joint action. Front Psychol 7, 1835 (2016). https://doi.org/10.3389/fpsyg.2016.01835

    Article  Google Scholar 

  28. 28.

    N. Marwan, J. Kurths, Nonlinear analysis of bivariate data with cross recurrence plots, Phys Lett A 302 (2002) 299–307. https://doi.org/10.1016/S0375-9601(02)01170-2

  29. 29.

    M.I. Coco, R. Dale, Cross-recurrence quantification analysis of categorical and continuous time series: An R package. Front Psychol 5, 510 (2014). https://doi.org/10.3389/fpsyg.2014.00510

    Article  Google Scholar 

  30. 30.

    M.-E. Nilsback, A. Zisserman, A visual vocabulary for flower classification. 2006 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit (CVPR’06), New York, NY, (2006) pp. 1447–1454. https://doi.org/10.1109/CVPR.2006.42

  31. 31.

    X. Zheng, Y. Wang, G. Wang, J. Liu, Fast and robust segmentation of white blood cell images by self-supervised learning. Micron. 107, 55–71 (2018). https://doi.org/10.1016/J.MICRON.2018.01.010

    Article  Google Scholar 

  32. 32.

    D.S. Chabot-Richards, T.I. George, Leukocytosis. Int J Lab Hematol 36, 279–288 (2014). https://doi.org/10.1111/ijlh.12212

    Article  Google Scholar 

  33. 33.

    M.D. Kumar, M. Babaie, S. Zhu, S. Kalra, H.R. Tizhoosh, A comparative study of CNN, BoVW and LBP for classification of histopathological images, 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI pp. 1-7, (2017).

  34. 34.

    T.J. Alhindi, S. Kalra, K.H. Ng, A. Afrin, H.R. Tizhoosh, Comparing LBP, HOG and deep features for classification of histopathology images, ArXiv:1805.05837v1. (2018)

  35. 35.

    T. Ojala, M. Pietikäinen, D. Harwood, A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29, 51–59 (1996). https://doi.org/10.1016/0031-3203(95)00067-4

    Article  Google Scholar 

  36. 36.

    M. Arya, N. Mittal, G. Singh, Texture-based feature extraction of smear images for the detection of cervical cancer. IET Comput Vis 12, 1049–1059 (2018). https://doi.org/10.1049/iet-cvi.2018.5349

    Article  Google Scholar 

  37. 37.

    V. Singhal, P. Singh, Local binary pattern for automatic detection of acute lymphoblastic leukemia. 2014 Twent. Natl. Conf. Commun. (NCC). Kanpur (2014), pp. 1–5. https://doi.org/10.1109/NCC.2014.6811261

  38. 38.

    G. Zimmerman-Moreno, I. Marin, M. Lindner, I. Barshack, Y. Garini, E. Konen, A. Mayer, Automatic classification of cancer cells in multispectral microscopic images of lymph node samples. 2016 38th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), Orlando, FL (2016), pp. 3973–3976. https://doi.org/10.1109/EMBC.2016.7591597

  39. 39.

    G. Kylberg, I.-M. Sintorn, Evaluation of noise robustness for local binary pattern descriptors in texture classification. EURASIP J Image Video Process 2013, 17 (2013). https://doi.org/10.1186/1687-5281-2013-17

  40. 40.

    P. Golland, F. Liang, S. Mukherjee, D. Panchenko, in Learn. Theory. Permutation tests for classification (Springer, Berlin, Heidelberg, 2005), pp. 501–515. https://doi.org/10.1007/11503415_34

    Google Scholar 

  41. 41.

    M. Patrício, J. Pereira, J. Crisóstomo, P. Matafome, M. Gomes, R. Seiça, F. Caramelo, Using resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer 18, 29 (2018). https://doi.org/10.1186/s12885-017-3877-1

    Article  Google Scholar 

  42. 42.

    Y. Chen, T. Huang, K. Chang, Y. Tsai, H.A. Chen, B. Chen, in IEEE Winter Conf. Appl. Comput. Vis.. Quantitative analysis of automatic image cropping algorithms: A dataset and comparative study (2017), pp. 226–234

    Google Scholar 

  43. 43.

    I. Konvalinka, D. Xygalatas, J. Bulbulia, U. Schjødt, E.-M. Jegindø, S. Wallot, G. Van Orden, A. Roepstorff, Synchronized arousal between performers and related spectators in a fire-walking ritual. Proc Natl Acad Sci U S A 108, 8514–8519 (2011). https://doi.org/10.1073/pnas.1016955108

    Article  Google Scholar 

  44. 44.

    M.H. Trauth, A. Asrat, W. Duesing, V. Foerster, K.H. Kraemer, N. Marwan, M.A. Maslin, F. Schaebitz, Classifying past climate change in the Chew Bahir basin, southern Ethiopia, using recurrence quantification analysis. Clim Dyn, 1–16 (2019). https://doi.org/10.1007/s00382-019-04641-3

Download references

Acknowledgments

TC is a new investigator and would like to thank the Hotchkiss Brain Institute (HBI) and CaPRI for their continued support. I would also like to thank Leonardo A. Molina of the CSM Optogenetics Facility, and Dr. Vincent Ebacher of the HBI Advanced Microscopy Platform (AMP) for insightful discussions on image processing/analysis.

Funding

TC is funded by the Hotchkiss Brain Institute/Department of Clinical Neurosciences/Tourmaline Oil Chair in Parkinson’s Disease Pilot Research Fund Program and the Alberta Children’s Hospital Research Institute (ACHRI) Behaviour & The Developing Brain Pilot Research Program.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Taylor Chomiak.

Ethics declarations

Conflict of interest

The author declares that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chomiak, T. Recurrence quantification analysis statistics for image feature extraction and classification. Data-Enabled Discov. Appl. 4, 2 (2020). https://doi.org/10.1007/s41688-020-00037-z

Download citation

Keywords

  • Image
  • Classification
  • Feature
  • Recurrence
  • Quantification
  • Analysis
  • Healthcare