An Initial Study in Wood Tomographic Image Classification using the
SVM and CNN Techniques
Antonio Alberto Pereira Junior
a
and Marco Antonio Garcia de Carvalho
b
School of Technology, University of Campinas, R. Paschoal Marmo 1888, Limeira/SP, Brazil
Keywords:
Ultrasound Tomography, Wood Internal Pathologies, Image Interpolation, Data Augmentation, Classification.
Abstract:
The internal analysis of wood logs is an essential task in the field of forest assessment. To assist in the
identification of anomalies within wood logs, methods from the Non-Destructive Testing area can be used, as
the acoustic methods. The ultrasound tomography is an acoustic method that allows to evaluate the internal
conditions of wood logs, through the analysis of wave propagation, without being necessary to cause damage
to the specimen. The images generated by ultrasound tomography can be improved by spatial interpolation,
i.e., estimating the values of wave propagation not measured in the initial examination. In this paper we
present an initial study of classification techniques in order to identify tomographic images with anomalies.
In our approach we consider three different classifiers: k-Nearest-Neighbor (k-NN), Support Vector Machine
(SVM) and Convolutional Neural Network (CNN). Experiments were conducted comparing them by means
of metrics obtained from the confusion matrix. We build a dataset with 5000 images using data augmentation
process. The quantitative metrics demonstrate the effectiveness of CNN when compared with k-NN and SVM
classifiers.
1 INTRODUCTION
Non-Destructive Tests (NDTs) are studied as they al-
low the evaluation of the specimen while maintaining
its integrity. In woods, the use of non-destructive test-
ing is interesting because it allows to decide which
information is needed to characterize each wood and
to know how to use the information to explain the be-
havior of the wood (Bucur, 2006).
One of the most used NDT technique is the ul-
trasound tomography. The ultrasound tomography al-
low the evaluation of the internal condition in woods
by measuring the propagation time of the ultrasonic
pulse, which does not cause any damage to the ob-
ject. Usually, the images obtained by the ultrasound
tomography don’t describe exactly the image regions
associated with the anomaly. For this reason, the pres-
ence of an expert is necessary to identify an exact re-
gion of the defect based on the tomography image. A
strategy used in order to improve the quality of gen-
erated images by ultrasound tomography is data in-
terpolation. Some of the main techniques are Inverse
Distance Weighting (Shepard, 1968), Ellipse Based
a
https://orcid.org/0000-0002-2304-3068
b
https://orcid.org/0000-0002-1941-6036
Spatial Interpolation (Du et al., 2015), and Path Con-
textual Analysis (Strobel, 2017).
Alternatively to the visual analysis of tomographic
images, there is an increase in the use of digital im-
age processing techniques and data classification (Du
et al., 2019)(Mu et al., 2015). However, there are still
few works in the literature that address the classifica-
tion of image regions in wood logs.
The goal of this paper is to propose a classifica-
tion approach in order to identify images with de-
fects in woods from the images generated by ultra-
sound tomography. To achieve this goal, we accom-
plish an initial study using three different techniques:
k-Nearest-Neighbor (k-NN), Support Vector Machine
(SVM) and Convolutional Neural Network (CNN).
The main contributions of this initial study are: (i)
enable the use of CNN using a dataset with few im-
ages and obtained with data augmentation support;
(ii) compare different image classification strategies.
Therefore, this work compares the classification tech-
niques by means of metrics calculated from a confu-
sion matrix. Also, as a secondary objective, this work
intends to gather data from ultrasound experiments on
wooden logs to generate a dataset and make it pub-
licly available.
Pereira Junior, A. and Garcia de Carvalho, M.
An Initial Study in Wood Tomographic Image Classification using the SVM and CNN Techniques.
DOI: 10.5220/0010881700003124
In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages
575-581
ISBN: 978-989-758-555-5; ISSN: 2184-4321
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
575
The reminder of this paper is organized as follows:
In Section 2 we address some related works and ba-
sic concepts. We describe how a tomographic image
is obtained by means of an ultrasound inspection test.
A description of our proposed method is addressed in
Section 3. We present briefly each one of the classifi-
cation techniques used in this work. The experiments
and results are shown in Section 4. Finally, in Section
5 we presented our conclusions.
2 BACKGROUND AND RELATED
WORKS
In this section we introduce basics concepts of ultra-
sound tomography on woods, as well as we briefly de-
scribe data interpolation techniques used in this work.
Finally, some related works on wood classification are
presented.
2.1 Concepts of Ultrasound
Tomography
Tomography is an acoustic non-destructive technique
used to analyze the structure and composition of ob-
jects (Grangeat, 2010). The ultrasound tomography
allows to evaluate the internal conditions of wood logs
by means of an ultrasound test, illustrated in Figure 1.
Figure 1: Ultrasound test on wood logs. The yellow arrows
show the transducers used to measure the wave propagation
time. SOURCE: Adapted from (Secco, 2011).
The ultrasound test measures the wave propaga-
tion time in the wooden logs, known as Time Of Flight
(TOF). Therefore, the acoustic velocity can be deter-
mined as the distance between the transducers (path
length) is also known.
In addition, the ultrasound test is performed ac-
cording to a wood representation scheme, which
provides a guide for positioning the transducers.
The main existing schemes, or inspection grids, are
diffraction and straight grids(Secco, 2011). The in-
spection grids define routes or paths through which
wave propagation occurs and are important attributes
in the generation of tomographic images.
2.2 Interpolation and Image Creation
In this work, spatial interpolation will be used to es-
timate the acoustic velocity values of points that do
not belong to a diffraction grid route. We select two
different methods in order to interpolate the data: the
Inverse Distance Weighting and the Path Contextual
Analysis.
Inverse Distance Weighting - IDW
The IDW interpolation technique is simple and com-
putationally efficient. The value of a point to be inter-
polated is given by the weighted average of the known
values of its neighborhood (Shepard, 1968). In this
case, the weight refers to the distance between the
points.
Path Contextual Analysis
In this method, the interpolation is accomplished ac-
cording to the position of the unknown point, consid-
ering two different regions to be analyzed: near to the
bark and to the pith wood(Strobel, 2017). The Path
Contextual Analysis uses an Ellipse Based Spatial In-
terpolation Algorithm in order to estimate the velocity
values in unknown grid cells that represent unknown
points(Du et al., 2015). In this method it is considered
that the propagation of the ultrasonic pulse has an el-
liptical influence zone around the route generated by
the positioning of the transducers.
2.3 Related Works
There are several works related to the application of
ultrasound tomography in woods. Usually, the works
intends to solve problems regarding to the generation
of images through ultrasound tomography, improve-
ment of interpolation methods and evaluation of to-
mographic image quality.
The works related to the investigation of the ultra-
sound tomography in wood seeks to understand the
relationships between: the coupling of transducers in
wood, problems related to the anisotropy, signal at-
tenuation, number of measurement points, frequency
used in the test, arrangements or meshes for testing
and type of transducers ((Socco et al., 2004), (Bucur,
2005),(Lin et al., 2008),(Palma et al., 2018)).
There are works which the authors report the use
of algorithms to reconstruct the internal characteris-
tics of the woods. For those studies, spatial interpola-
tion algorithms are studied. In (Zeng et al., ) is pre-
sented an approach using affected areas through an
ellipse with the same eccentricity. This method is im-
proved by the authors (Du et al., 2015) considering
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
576
ellipses with different eccentricities, giving rise to the
Ellipse Based Spatial Interpolation (EBSI) method.
Finally, (Du et al., 2018) and (Du et al., 2019) present
some improvements to the EBSI method. As a way
of evaluating wood considering different axis, the au-
thor (Feng et al., 2018) performs an experiment with
the Radial and Longitudinal axis, and also proposes
the interpolation method Velocity Correction Interpo-
lation(VCI).
In general, methods are not quantitatively evalu-
ated in order to verify tomographic image quality. In
(Strobel et al., 2018) is presented a quantitative eval-
uation approach, using the confusion matrix, compar-
ing the tomography image with a ground-truth image.
There are also works that use artificial intelli-
gence (AI) methods to identify defects in wood (Zhu
et al., 2009), (Mu et al., 2015), and to reconstruct
the internal characteristics of wood (Effendi et al.,
2019),(Hansson et al., 2015). However, in these
works other inspection techniques are considered,
such as CT and X-Ray.
There’s an alternative for the reconstruction of to-
mography images using the technique of deep learn-
ing and contour constraint (DLCC) (Du et al., 2019).
In these work the deep learning algorithm was applied
to detect the size, texture and limit the contour of de-
fects in the wood.
Studies on ultrasound tomography are taking ad-
vantage of AI concepts as a tool to identify defects in
wood. However, there are opportunities to be devel-
oped, due to the low amount of works that approach
these techniques. In order to cover this gap, this work
intends to implement classification techniques for the
identification of tomographic images of wood logs
with defects.
3 PROPOSED APPROACH
This section discuss the approach proposed in this pa-
per. The workflow shown in the Figure 2 illustrates
a general view of each step of our work: (i) image
acquisition; (ii) feature extraction; (iii) classification
using k-NN, SVM and CNN; and (iv) performance
evaluation.
Classification Model
Evaluation
Dataset
Feature Extraction
CNN
k-NN & SVM
Performance comparison using
Confusion Matrix
A
Figure 2: Workflow of the proposed approach.
The following subsections explain each step of our
proposed approach.
3.1 Dataset and Evaluation Protocol
The image were obtained from the Non-Destructive
Laboratory (LabEND) of the University of Campinas
- UNICAMP. Table 1 show the wood species and pro-
vides a brief description of the wood logs used in this
work.
Table 1: Wood logs used in the experiments. SOURCE:
(Strobel, 2017).
Species Description
Lonchocarpus It has artificial circular hollows 5cm
in diameter.
Liquidambar styraci-
flua
Small area with an early stage of
fungal decomposition near to the
pith, plus lateral cracks from pith to
bark.
Platanus sp Most of the wood shows signs of
fungus attacks and there are some
empty areas caused by termites.
Centrolobium sp Contains central hollow due to ter-
mite attack.
Due to the limited amount of images, a process of
data augmentations is also required to be able to in-
crease the size and the quality of data (Shorten and
Khoshgoftaar, 2019). This process consists of manip-
ulating an existing image by submitting it to geomet-
ric transformations, i.e., rotation and resizing. The ad-
dition of noise is another technique used to increase
the amount of images, one of the most known tech-
niques is the interference of Salt & Pepper, which
consists of adding white and black pixels in a sparse
occurrence. Labelling is the last step to create the
dataset.
First, it’s necessary to determine an area in the
ultrasound tomography image and associate to the
“anomaly” class. An initial labeling process had been
done in previous work (Strobel, 2017) and served as
the reference for the dataset annotation.
Finally, three metrics were employed to evaluate
the performance of the classification technique, com-
paring its results with the ground-truth provided by
the labelling process: (1) recall - R; (2) precision - P;
and (3) accuracy - Acc. The equations are defined as
follows:
R =
T P
T P + FP
(1)
P =
T P
T P + FN
(2)
Acc =
T P + T N
T P + T N + FP + FN
(3)
An Initial Study in Wood Tomographic Image Classification using the SVM and CNN Techniques
577
where TP, TN, FP and FN represents, respectively,
true-positive, true-negative, false-positive, and false-
negative values.
3.2 Feature Extraction
The purpose of this step is to obtain descriptors that
are able to represent the texture characteristics of the
tomographic image. The following subsections de-
scribes the texture descriptor we use in this work: the
Gray Levels Co-occurrence Matrix (GLCM) and the
Local Binary Pattern (LBP).
3.2.1 Gray Levels Co-occurrence Matrix
Gray Level Co-occurrence Matrix (GLCM) is a
method of extracting texture features from grayscale
image (Robert et al., 1973). Through each vec-
tor of characteristics obtained, another 14 charac-
teristics can be obtained from the generated matrix,
among them: Mean, Variance, Entropy, Dissimilarity,
Contrast, Homogeneity, Correlation, Energy, Clus-
ter Shade, Cluster Prominence, Sum Entropy, Sum
Mean, Entropy Difference, Sum Variance.
3.2.2 Local Binary Pattern
Local Binary Pattern (LBP) is a method to describe
the local image pattern (Ojala et al., 1996). LBP
assumes that textures can be described by measure-
ments: local spatial patterns and gray level contrast.
This method extracts local texture information and
sets a threshold for a number of neighbors, in the
value of the central pixel in that defined local neigh-
borhood.
3.3 Classification Techniques
Classification techniques are use to classify datasets
with known definitions of groups. In this case it’s
presented to the model samples of desired inputs and
outputs, previous defined by an human. The goal of
this technique is to learn a general rule that map the
inputs and outputs to the overall model.
This work intends to detect the region of the im-
age that has the anomaly. Therefore, classifications
models will be used to execute this proposal. We
use two supervised algorithms: k-Nearest-Neighbor
(k-NN) and Support Vector Machines (SVM). Those
two algorithms was chosen due to common use in the
classification tasks. Additionally, as a tentative to im-
prove the classification performance, a Convolutional
Neural Network (CNN) model is also applied in this
work. By the end, we should be able to compare both
models and then determine which one is the best to
identify anomaly in woods. The following subsection
describes each one of the classification techniques.
3.3.1 k-NN
The k-Nearest-Neighbor (k-NN) (Cover and Hart,
1967) is also known as lazy learn or instance-based,
which means that the algorithm does not perform the
model definition or generalization when training data
is received, but during execution time. This analogi-
cal learning technique performs peer-to-peer compar-
isons to identify similarities between input data. To
determine which data are closest or similar, it is nec-
essary to describe them using distance notation, i.e.,
the euclidean distance.
3.3.2 Support Vector Machine
The Support Vector Machines (SVM) (Suykens and
Vandewalle, 1999) is a classification technique that
can be applied to linearly separable or non-linearly
separable data. This technique aims to determine the
best hyperplane for class segregation using vectors to
support this determination. In this work, we use dif-
ferent kernels to see the performance of each one sep-
arately.
3.3.3 Convolutional Neural Network
The Convolutional Neural Network (CCN) is one of
the most popular deep neural network. This network
can have multiple layer, in particular: the convolu-
tion layer, ReLU, pooling layer and fully connected
layer (Albawi et al., 2017). The CNN have the ability
to train large datasets and considers millions of pa-
rameters, because of that, this network is commonly
used in area of image recognition, object detection
and computer vision.
A CNN performs automatically the step of fea-
ture extraction by using the convolution layer. So
the model can learn all features in one pass instead
of having the features being selected by an engineer.
For those aspects, a Convolutional Neural Network
model is going to be used in this work to avoid the
features extraction step. In contrast, the need for a
large amount of training data is an important feature
of CNNs. In order to mitigate this requirement, we
apply data augmentation techniques in our dataset.
The Convolutional Neural Network that will be used
in this work is the SDD MobileNetV2 (Sandler et al.,
2018). This Convolution Neural Network is available
in Tensorflow 2 (Abadi et al., 2015) which uses the
object detection approach to detect bounding boxes.
This model was chosen due to the speed and to the in-
put size of tomography image available in the dataset.
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
578
4 EXPERIMENTS AND INITIAL
RESULTS
This section presents the conducted experiments re-
garding the initial study of different classification
techniques to assess the tomographic images. All
experiments are performed using an Intel i5 desk-
top, 16GB RAM running macOS 10.15 Catalina.
Our computational implementation was developed
in Python 3.0 using Google Colaboratory (Bisong,
2019). We present the results and compare the per-
formance of the techniques with each other.
As mentioned before, a data augmentation process
was used to build the entire dataset. At the end of
this process, the dataset had 5000 tomographic im-
ages and the annotation files related to them.
The first step for the common supervised models
is to extract the vector of features from each tomo-
graphic image. To be able to extract those feature
vectors it was necessary to get the features that be-
longs to the anomaly and the features that correspond
to the healthy wood region. Those vectors generated
by the feature extraction are the input for the classifi-
cation models: SVM and k-NN. The dataset was split
in 70% of images for training and 30% for test step.
Our first experiment concerns studying different
types of SVM usage configurations. Table 2 shows
the results of the SVM model using different types of
Kernel. We evaluate the performance of the classi-
fication task under the different metrics described in
the section 3.1. The best performance for each metric
are highlighted in bold.
Table 2: Effectiveness of the SVM technique using different
combinations (in bold, the highest value observed in each
column).
SVM (Kernel) - Feature Accuracy Recall Precision
SVM (Linear) - LBP 76.00 87.80 73.46
SVM (Linear) - GLCM 74.66 88.09 72.54
SVM (Polynomial) - LBP 85.33 92.68 82.60
SVM (Polynomial) - GLCM 68.00 64.28 75.00
SVM (RBF) - LBP 86.66 95.12 82.97
SVM (RBF) - GLCM 81.33 88.09 80.43
SVM (Sigmoid) - LBP 64.00 60.97 69.44
SVM (Sigmoid) - GLCM 64.00 66.66 68.29
According to Table 2, the best result of Accuracy,
Recall and Precision occurred using the SVM with the
Kernel RBF and the LBP feature. The second exper-
iment analyzes the use of the k-NN classifier. Table
3 shows the results of the k-NN model. The values
of k was chosen based on a experiment with a small
portion of the dataset. This experiment showed lim-
ited improvements in the accuracy when k was incre-
mented with a values less than 50 for each run. For
this reason the experiment showing in the table be-
low for the classifier k-NN is going to consider k=50,
k=100, and k=150.
Table 3: Effectiveness of the k-NN technique using different
combinations (in bold, the highest value observed in each
column).
k-NN (k=Value) - Feature Accuracy Recall Precision
k-NN (k=50) - LBP 78.66 85.36 77.77
k-NN (k=50) - GLCM 74.66 76.19 78.04
k-NN (k=100) - LBP 74.66 80.48 75.00
k-NN (k=100) - GLCM 54.66 45.23 66.33
k-NN (k=150) - LBP 57.33 43.90 66.66
k-NN (k=150) - GLCM 49.33 16.66 70.00
In the case of the k-NN classifier, the best result
was the one that use LBP considering k=50. Over-
all, the results shown in Tables 2 and 3 presents good
accuracy values, especially those using LBP descrip-
tor. The last classification method addressed in this
work was the CNN. In this case, our experiment con-
sists of comparing the best performances of SVM and
k-NN, using LBP texture descriptor, with the results
obtained by using the CNN SDD MobileNetV2. Ta-
ble 4 shows the results.
Table 4: Performance comparison (in bold, the highest
value observed in each column).
Technique Accuracy Recall Precision
SVM (RBF) - LBP 86.66 95.12 82.97
k-NN (k=50) - LBP 78.66 85.36 77.77
SDDMobileNetV2 89.00 93.40 97.30
As we can observe in Table 4, the performance of
the SDDMobileNetV2 classifier was better than that
obtained by the other classifiers, regardless the used
metric, Accuracy, Recall or Precision. Nevertheless,
the three first experiments concerns to the task of clas-
sify the entire image as having or not an anomaly.
That is, the results do not show the specific image re-
gion associated to the anomaly. Therefore, in order
to identify the anomaly in the image, we propose a
last experiment: perform the classification of image
regions, obtained from Otsu algorithm (Otsu, 1979).
Figure 3 shows the results of the region classifi-
cation process using the SVM plus LBP feature (a),
and for the SDDMobileNetV2 classifier (b). In these
cases, the presence of an internal defect in the central
region of the wood is observed. This information is
important from a wood usage classification point of
view, for commercial purposes.
5 CONCLUSIONS
In this work we have presented an initial study about
the use of data classification techniques in wood to-
An Initial Study in Wood Tomographic Image Classification using the SVM and CNN Techniques
579
(a) (b)
Figure 3: Classification of the anomaly image region us-
ing two different techniques for the Platanus sp. wood: (a)
SVM with LBP descriptor (region contour in green); (b)
CNN with SDDMobileNetV2 architecture (bounding box
area).
mography images. Our dataset consists of images ob-
tained from ultrasound tomography, a non-destructive
method capable of evaluating the wood log internal
characteristics without causing any damage to it.
In order to identify whether or not an image has
anomalies, we applied three different image classi-
fication methods: k-NN, SVM and CNN. The per-
formance of these methods were evaluated according
to Accuracy, Precision and Recall metrics, computed
from a confusion matrix built based on the annotated
images. We also performed an image region classifi-
cation task, in order to obtain the region correspond-
ing to the wood anomaly.
Our first experiments showed that the best results
are obtained by the CNN classifier, regardless the
metric. The accuracy, precision and recall values are
higher than 85%. A last experiment carried out in this
work was dedicated to identifying the region in the
image associated with the internal defect.
Our contribution is also associated with the cre-
ation of a dataset with about 5000 images using data
augmentation techniques. Now, our efforts will be to-
wards characterizing and balancing the dataset, avoid-
ing possible biases.
There are several possible suggestions as future
works. First one, in order to improve the variability
of the dataset, we need more wood tomographic im-
ages, and different species and anomalies.
The use of texture descriptors of different types,
such as those provided by Fourier and Wavelet Trans-
forms, should be the object of further studies. We
also intend to combine different descriptors in order
to verify the classification performance.
In addition to classifying the wood image as
healthy or with an internal defect, we would like to
properly identify the anomaly, its location and dimen-
sions.
ACKNOWLEDGEMENTS
This study was financed in part by the Coordenac¸
˜
ao
de Aperfeic¸oamento de Pessoal de N
´
ıvel Superior
Brasil (CAPES) – Finance Code 001.
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