Automated classification of wood transverse cross-section micro-imagery from 77 commercial Central-African timber species

  • Núbia Rosa da Silva
  • Maaike De Ridder
  • Jan M. Baetens
  • Jan Van den Bulcke
  • Mélissa Rousseau
  • Odemir Martinez Bruno
  • Hans Beeckman
  • Joris Van Acker
  • Bernard De Baets
Original Paper


Key message

Pattern recognition has become an important tool to aid in the identification and classification of timber species. In this context, the focus of this work is the classification of wood species using texture characteristics of transverse cross-section images obtained by microscopy. The results show that this approach is robust and promising.


Considering the lack of automated methods for wood species classification, machine vision based on pattern recognition might offer a feasible and attractive solution because it is less dependent on expert knowledge, while existing databases containing high-quality microscopy images can be exploited.


This work focuses on the automated classification of 1221 micro-images originating from 77 commercial timber species from the Democratic Republic of Congo.


Microscopic images of transverse cross-sections of all wood species are taken in a standardized way using a magnification of 25 ×. The images are represented as texture feature vectors extracted using local phase quantization or local binary patterns and classified by a nearest neighbor classifier according to a triplet of labels (species, genus, family).


The classification combining both local phase quantization and linear discriminant analysis results in an average success rate of approximately 88% at species level, 89% at genus level and 90% at family level. The success rate of the classification method is remarkably high. More than 50% of the species are never misclassified or only once. The success rate is increasing from the species, over the genus to the family level. Quite often, pattern recognition results can be explained anatomically. Species with a high success rate show diagnostic features in the images used, whereas species with a low success rate often have distinctive anatomical features at other microscopic magnifications or orientations than those used in our approach.


This work demonstrates the potential of a semi-automated classification by resorting to pattern recognition. Semi-automated systems like this could become valuable tools complementing conventional wood identification.


Commercial timber species Democratic Republic of Congo Image analysis Pattern recognition Transverse cross-section Wood anatomy 



We thank Kévin Liévens from the RMCA for the preparation and sectioning of wood blocks, formerly without thin sections.

Compliance with Ethical Standards


This work was supported by the São Paulo Research Foundation (FAPESP) (Grants: 2011/01523-1, 2011/21467-9 and 2014/06208-5), the National Council for Scientific and Technological Development (CNPq) (Grants: 308449/2010-0 and 484312/2013-8) and the Belgian Science Policy Belspo (AG/LL/165, BR/143/A3/HERBAXYLAREDD and DGD-RMCA project: “Visual Wood Identification key”).


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Copyright information

© INRA and Springer-Verlag France 2017

Authors and Affiliations

  • Núbia Rosa da Silva
    • 1
    • 2
    • 3
  • Maaike De Ridder
    • 4
    • 5
  • Jan M. Baetens
    • 6
  • Jan Van den Bulcke
    • 4
  • Mélissa Rousseau
    • 5
  • Odemir Martinez Bruno
    • 1
    • 2
  • Hans Beeckman
    • 5
  • Joris Van Acker
    • 4
  • Bernard De Baets
    • 6
  1. 1.Institute of Mathematics and Computer ScienceUniversity of São Paulo, USPSão CarlosBrazil
  2. 2.Scientific Computing Group, São Carlos Institute of PhysicsUniversity of São Paulo, USPSão CarlosBrazil
  3. 3.Institute of BiotechnologyFederal University of Goiás, UFGCatalãoBrazil
  4. 4.Department of Forest and Water Management, Faculty of Bioscience EngineeringGhent UniversityGhentBelgium
  5. 5.Royal Museum for Central Africa, Service of Wood BiologyTervurenBelgium
  6. 6.KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience EngineeringGhent UniversityGhentBelgium

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