Texture Analysis by a PLS Based Method for Combined Feature Extraction and Selection

  • Joselene Marques
  • Erik Dam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)


We present a methodology that applies machine-learning techniques to guide partial least square regression (PLS) for feature extraction combined with feature selection. The developed methodology was evaluated in a framework that supports the diagnosis of knee osteoarthritis (OA). Initially, a set of texture features are extracted from the MRI scans. These features are used for segmenting the region-ofinterest and as input to the PLS regression. Our method uses PLS output to rank the features and implements a learning step that iteratively selects the most important features and applies PLS to transform the new feature space. The selected bone texture features are used as input to a linear classifier trained to separate the subjects in healthy or OA. The developed algorithm selected 18% of the initial feature set and reached a generalization area-under-the-ROC of 0.93, which is higher than established markers known to relate to OA diagnosis.


machine learning PLS classification feature extraction feature selection texture analysis OA bone structure 


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  1. 1.
    Schad, L.R., Blüml, S., Zuna, I.: MR tissue characterization of intracranial tumors by means of texture analysis. Magnetic Resonance Imaging 11(6), 889–896 (1993)CrossRefGoogle Scholar
  2. 2.
    Herlidou, S., Rolland, Y., Bansard, J.Y., Le Rumeur, E., de Certaines, J.D.: Comparison of automated and visual texture analysis in MRI: Characterization of normal and diseased skeletal muscle. Magnetic Resonance Imaging 17(9), 1393–1397 (1999)CrossRefGoogle Scholar
  3. 3.
    Kovalev, V.A., Kruggel, F., von Cramon, D.: Gender and age effects in structural brain asymmetry as measured by MRI texture analysis. NeuroImage 19(3), 895–905 (2003)CrossRefGoogle Scholar
  4. 4.
    Herlidou, S., Grebe, R., Grados, F., Leuyer, N., Fardellone, P., Meyer, M.E.: Influence of age and osteoporosis on calcaneus trabecular bone structure: a preliminary in vivo MRI study by quantitative texture analysis. Magnetic Resonance Imaging 22(2), 237–243 (2004)CrossRefGoogle Scholar
  5. 5.
    Sørensen, L., Shaker, S.B., de Bruijne, M.: Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Transactions on Medical Imaging 29(2), 559–569 (2010)CrossRefGoogle Scholar
  6. 6.
    Beyer, K.S., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is ”nearest neighbor” meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  7. 7.
    Liu, H., Motoda, H.: Feature Extraction, Construction and Selection: A Data Mining Perspective. Kluwer Academic Publishers, Norwell (1998)CrossRefzbMATHGoogle Scholar
  8. 8.
    Hubert, M., Branden, K.V.: Robust methods for partial least squares regression. Journal of Chemometrics 17(10), 537–549 (2003)CrossRefGoogle Scholar
  9. 9.
    Li, H., Liang, Y., Xu, Q., Cao, D.: Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta 648(1), 77–84 (2009)CrossRefGoogle Scholar
  10. 10.
    Lindgren, F., Geladi, P., Berglund, A., Sjostrom, M., Wold, S.: Interactive variable selection (IVS) for PLS. Part 1: Theory and algorithms. Journal of Chemometrics 8, 349–363 (1994)CrossRefGoogle Scholar
  11. 11.
    Wold, S.: PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58(2), 109–130 (2001)CrossRefGoogle Scholar
  12. 12.
    Bryan, K., Brennan, L., Cunningham, P.: Metafind: A feature analysis tool for metabolomics data. BMC Bioinformatics 9 (2008)Google Scholar
  13. 13.
    Chong, I.G., Jun, C.H.: Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory System 78(1-2), 103–112 (2005)CrossRefGoogle Scholar
  14. 14.
    Kellgren, J.H., Lawrence, J.S.: Radiological assessment of osteo-arthrosis. Annals of the Rheumatic Diseases 16(4), 494–502 (1957)CrossRefGoogle Scholar
  15. 15.
    Folkesson, J., Dam, E.B., Olsen, O.F., Pettersen, P.C., Christiansen, C.: Segmenting articular cartilage automatically using a voxel classification approach. IEEE Transactions on Medical Imaging 26, 106–115 (2007)CrossRefGoogle Scholar
  16. 16.
    Florack, L., ter Haar Romeny, B., Viergever, M., Koenderink, J.: The Gaussian scale-space paradigm and the multiscale local jet. International Journal of Computer Vision 18(1), 61–75 (1996)CrossRefGoogle Scholar
  17. 17.
    Weickert, J.: Anisotropic Diffusion in Image Processing. B.G.Teubner Stuttgart (1998)Google Scholar
  18. 18.
    Dam, E.B., Loog, M., Christiansen, C., Byrjalsen, I., Folkesson, J., Nielsen, M., Qazi, A., Pettersen, P.C., Garnero, P., Karsdal, M.A.: Identification of progressors in osteoarthritis by combining biochemical and MRI-based markers. Arthritis Research & Therapy 11(4), R115 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Joselene Marques
    • 1
    • 2
  • Erik Dam
    • 2
  1. 1.University of CopenhagenDenmark
  2. 2.BiomedIQCopenhagenDenmark

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