NDLI: Identification of selected internal wood characteristics in computed tomography images of black spruce: a comparison study

Content Provider	Springer Nature Link
Author	Wei, Qiang Chui, Ying Hei Leblon, Brigitte Zhang, Shu Yin
Copyright Year	2009
Abstract	The feasibility of identifying internal wood characteristics in computed tomography (CT) images of black spruce was investigated using two promising classifiers: the maximum likelihood classifier (MLC) and the back propagation (BP) artificial neural network (ANN) classifier. Nine image features including one spectral feature (gray level values), a distance feature, and seven textural features were employed to develop the classifiers. The selected internal wood characteristics to be identified included heartwood, sapwood, bark, and knots. Twenty cross-sectional CT images of a black spruce log were randomly selected to develop the two classifiers. The results suggest that both classifiers produced high classification accuracy. Compared with the MLC classifier (80.9% overall accuracy), the BP ANN classifier had better classification performance (97.6% overall accuracy). Moreover, statistical analysis reveals that the heartwood of the black spruce log used in this study is the easiest to identify by either classifier compared with the other three log features. The results also suggest that the separability of one wood characteristic from the other wood characteristics in black spruce CT images is mainly related to moisture content.
Starting Page	175
Ending Page	180
Page Count	6
File Format	PDF
ISSN	14350211
Journal	Journal of Wood Science
Volume Number	55
Issue Number	3
e-ISSN	16114663
Language	English
Publisher	Springer Japan
Publisher Date	2009-02-11
Publisher Place	Japan
Access Restriction	Subscribed
Subject Keyword	Artificial neural network (ANN) Black spruce Computed tomography (CT) images Internal wood features Maximum likelihood classifier (MLC) Characterization and Evaluation of Materials Materials Science Wood Science & Technology
Content Type	Text
Resource Type	Article
Subject	Biomaterials Forestry

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