Neural Processing Letters

, Volume 38, Issue 3, pp 375–387 | Cite as

Extreme Learning Machines for Feature Selection and Classification of Cocaine Dependent Patients on Structural MRI Data

  • M. Termenon
  • M. GrañaEmail author
  • A. Barrós-Loscertales
  • C. Ávila


In this paper, we present a Computer Aided Diagnosis and image biomarker identification system for cocaine dependence, which selects relevant regions from a set of brain structural magnetic resonance images (sMRI). After sMRI volume preprocessing for spatial normalization, we compute Pearson’s correlation between pixel values across volumes and the indicative variable, obtaining a volume of correlation values (VCV). We calculate the gradient of the VCV which is used to perform a watershed segmentation of the brain volume into regions. A region selection stage finds the most relevant watershed regions. We propose two different approaches to characterize region relevance: (a) a wrapper procedure using extreme learning machines (ELM), and (b) apply correlation distribution percentiles to select most discriminant regions. Next, we consider three different procedures to extract the image features corresponding to selected regions: (1) collecting the sMRI intensity values of all the voxels that compose each region, compute (2) the mean or (3) the median of the sMRI intensity value of the voxels contained in each selected region. Extracted feature vectors are used to build a classifier aiming to discriminate between cocaine dependent patients and healthy controls. We compare results of several classifiers: ELM, OP-ELM, SVM and 1NN. Also, we visualize the brain locations of selected regions, checking if these locations are in accordance with previous findings in the medical literature.


Extreme learning machine Neuroimaging Cocaine dependence 


  1. 1.
    Barrós-Loscertales A, Garavan H, Bustamante JC, Ventura-Campos N, Llopis JJ, Belloch V, Parcet MA, Ávila C (2011) Reduced striatal volume in cocaine-dependent patients. NeuroImage 56:1021–1026CrossRefGoogle Scholar
  2. 2.
    Franklin TR, Acton PD, Maldjian JA, Gray JD, Croft JR, Dackis CA, O’Brien CP, Childress AR (2002) Decreased gray matter concentration in the insular, orbitofrontal, cingulate, and temporal cortices of cocaine patients. Biol Psychiatr 51:134–142CrossRefGoogle Scholar
  3. 3.
    Besga A, Termenon M, Graña M, Echeveste J, Pérez J, Gonzalez-Pinto A (2012) Discovering Alzheimer’s disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features. Neurosci Lett 520:71–76CrossRefGoogle Scholar
  4. 4.
    Graña M, Termenon M, Savio A, Gonzalez-Pinto A, Echeveste J, Pérez JM, Besga A (2011) Computer aided diagnosis system for alzheimer disease using brain diffusion tensor imaging features selected by pearson’s correlation. Neurosci Lett 502:225–229CrossRefGoogle Scholar
  5. 5.
    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182zbMATHGoogle Scholar
  6. 6.
    Savio A, Charpentier J, Termenon M, Shinn AK, Graña M (2010) Neural classifiers for schizophrenia diagnostic support on diffusion imaging data. Neural Netw World 20:935–949Google Scholar
  7. 7.
    Davatzikos C, Fan Y, Wu X, Shen D, Resnick S (2008) Detection of prodromal alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiol Aging 29:514–523CrossRefGoogle Scholar
  8. 8.
    Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13:583–598CrossRefGoogle Scholar
  9. 9.
    Shafarenko L, Petrou M, Kittler J (1997) Automatic watershed segmentation of randomly textured color images. IEEE Trans Image Process 6:1530–1544CrossRefGoogle Scholar
  10. 10.
    Fan Y, Shen D, Gur RC, Gur RE, Davatzikos C (2007) COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Trans Med Imaging 23:3–105Google Scholar
  11. 11.
    Grau V, Mewes A, Alcaniz M, Kikinis R, Warfield S (2004) Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imaging 23:447–458CrossRefGoogle Scholar
  12. 12.
    Sijbers J, Scheunders P, Verhoye M, van der Linden A, van Dyck D, Raman E (1997) Watershed-based segmentation of 3D MR data for volume quantization. Magn Reson Imaging 15(6):679–688CrossRefGoogle Scholar
  13. 13.
    Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRefGoogle Scholar
  14. 14.
    Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122CrossRefGoogle Scholar
  15. 15.
    Chen F, Ou T (2011) Sales forecasting system based on gray extreme learning machine with taguchi method in retail industry. Expert Syst Appl 38:1336–1345CrossRefGoogle Scholar
  16. 16.
    Sun Z-L, Choi T-M, Au K-F, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst 46:411–419CrossRefGoogle Scholar
  17. 17.
    Wong W, Guo Z (2010) A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. Int J Prod Econ 128:614–624CrossRefGoogle Scholar
  18. 18.
    Zong W, Huang G-B (2011) Face recognition based on extreme learning machine. Neurocomputing 74:2541–2551CrossRefGoogle Scholar
  19. 19.
    Marques I, Graña M (2012) Face recognition with lattice independent component analysis and extreme learning machines. Soft Comput. 16(9): 1525–1537Google Scholar
  20. 20.
    Mohammed A, Minhas R, Jonathan WuQ, Sid-Ahmed M (2011) Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recognit 44:2588–2597CrossRefzbMATHGoogle Scholar
  21. 21.
    Yuan Q, Zhou W, Li S, Cai D (2011) Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res 96:29–38CrossRefGoogle Scholar
  22. 22.
    Shi L-C, Lu B-L (2013) EEG-based vigilance estimation using extreme learning machines. Neurocomputing 102:135–143CrossRefGoogle Scholar
  23. 23.
    Cao J, Lin Z, Huang G-B, Liu N (2012) Voting based extreme learning machine. Inf Sci 185:66–77MathSciNetCrossRefGoogle Scholar
  24. 24.
    Huang G-B, Wang D (2011) Advances in extreme learning machines (ELM2010). Neurocomputing 74:2411–2412CrossRefGoogle Scholar
  25. 25.
    Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70:3056–3062Google Scholar
  26. 26.
    Huang G-B, Li M-B, Chen L, Siew C-K (2008) Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing 71:576–583CrossRefGoogle Scholar
  27. 27.
    Huang G-B, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74:155–163CrossRefGoogle Scholar
  28. 28.
    Lan Y, Soh YC, Huang G-B (2009) Ensemble of online sequential extreme learning machine. Neurocomputing 72:3391–3395CrossRefGoogle Scholar
  29. 29.
    Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A (2010) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21:158–162CrossRefGoogle Scholar
  30. 30.
    Ashburner J, Friston KJ (2005) Unified segmentation. NeuroImage 26:839–851CrossRefGoogle Scholar
  31. 31.
    Beucher S, Lantuejoul C (1979) Use of watersheds in contour detection. In: International Workshop on image processing, real-time edge and motion detection/estimation, Rennes, France, SeptemberGoogle Scholar
  32. 32.
    Meyer F (1994) Topographic distance and watershed lines. Signal Process 38:113–125CrossRefzbMATHGoogle Scholar
  33. 33.
    Oldfield RC (1971) The assessment and analysis of handedness: the edinburgh inventory. Neuropsychologia 9:97–113CrossRefGoogle Scholar
  34. 34.
    Van Rijsbergen C (1979) Information retrieval, Butterworth-Heinemann, OxfordGoogle Scholar
  35. 35.
    Vapnik VN (1998) Statistical learning theory. Wiley-Interscience, New YorkzbMATHGoogle Scholar
  36. 36.
    Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybernet B 42(2):513–529CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • M. Termenon
    • 1
  • M. Graña
    • 1
    Email author
  • A. Barrós-Loscertales
    • 2
  • C. Ávila
    • 2
  1. 1.Computational Intelligence GroupUniversidad del País VascoSan SebastiáinSpain
  2. 2.Departamento Psicología Básica, Clínica y PsicobiologíaUniversitat Jaume ICastellón de la PlanaSpain

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