J. Biomedical Science and Engineering, 2013, 6, 1099-1108 JBiSE
http://dx.doi.org/10.4236/jbise.2013.611138 Published Online November 2013 (http://www.scirp.org/journal/jbise/)
3D segmentation and visualization of lung and its
structures using CT images of the thorax
Pedro P. Rebouças Filho1, Paulo Cesar Cortez2, Victor Hugo C. de Albuquerque3*
1Industry Area, Federal Institute of Education Science and Technology of Ceará, Maracanaú, Brazil
2Teleinformatic Engineering Department, Federal University of Ceará, Fortaleza, Brazil
3Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza, Brazil
Email: pedrosarf@ifce.edu.br, cortez@deti.ufc.br, *victor.albuquerque@unifor.br
Received 25 September 2013; revised 26 October 2013; accepted 7 November 2013
Copyright © 2013 Pedro P. Rebouças Filho et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computing systems have been playing an important
role in various medical fields, notably in image diag-
nosis. Studies in the field of Computational Vision
aim at developing techniques and systems capable of
detecting various illnesses automatically. What has
been highlighted among the existing exams that allow
diagnosis aid and the application of computing sys-
tems in parallel is Computed Tomography (CT). CT
enables the visualization of internal organs, such as
the lung and its structures. Computational Vision
systems extract information from the CT images by
segmenting the regions of interest, and then recognize
and identify details in those images. This work fo-
cuses on the segmentation phase of CT lung images
with singularity-based techniques. Among these me-
thods are the region growing (RG) technique and its
3D RG variations and the thresholding technique
with multi-thresholding. The 3D RG method is appli-
ed to lung segmentation and from the 3D RG seg-
ments of the lung hilum, the multi-thresholding can
segment the blood vessels, lung emphysema and the
bones. The results of lung segmentation in this work
were evaluated by two pulmonologists. The results ob-
tained showed that these methods can integrate aid
systems for medical diagnosis in the pulmonology field.
Keywords: 3D Region Growing; Lungs segmentation;
COPD; Pulmonary Structure Visualization; Computed
A large number of diseases that affect the world’s po-
pulation are lung-related. Therefore, research in the field
of pulmonology has great importance in public health
studies and focuses mainly on asthma, bronchiectasis and
Chronic Obstructive Pulmonary Disease (COPD) [1,2].
The World Health Organization (WHO) estimates that
there are 300 million people who suffer from asthma,
and this disease causes around 250 thousand deaths per
year worldwide [3]. Also, WHO estimates that 210
million people have COPD. This disease caused the
death of over 300 thousand people in 2005 [4]. Recent
studies reveal that COPD is present in the 20- to 45-year-
old age bracket, although it is characterized as an over-
50-year-old disease. Based on this, WHO estimates that
the number of deaths due to COPD will increase 30% by
2015, and by 2030 COPD will be the third cause of mor-
talities worldwide [5].
During the period from 1992 to 2006, 15% of all
hospitalizations financed by the Brazilian Federal Health
System (SUS) were due to pulmonary diseases, of which
asthma and COPD together summed up 562,016 hospita-
lizations [3].
Thus, it is of fundamental importance for the public
health system to obtain an early and correct diagnosis of
any pulmonary disease. Diagnosis aid is important from
a clinical point of view as it increases the amount of in-
formation the specialist has concerning the patient’s state
of health. Therefore, certain illnesses can be detected
precociously, even saving lives in some cases. Besides,
some techniques allow the clinical image of the disease
to be tracked appropriately [6,7].
The segmentation stage pulmonology systems are es-
sential for the correct and accurate medical diagnosis aid,
as this stage delimits the lung area in CT images of the
thorax, which must be analyzed by the system or by a
The segmentation of objects and structures in medical
images is a process that, in most cases, is more complex
if compared to the segmentation of other image types,
*Corresponding author.
P. P. Rebouças Filho et al. / J. Biomedical Science and Engineering 6 (2013) 1099-1108
such as metallographic and synthetic aperture radar re-
mote sensing. This occurs due to the variability of struc-
tures and/or internal organs, the visualization plane of
these images and furthermore, the possibility of diseases
affecting these organs. All of which contribute to in-
creasing the difficulty to develop efficient techniques for
medical image segmentation [8].
CT images of the lungs represent a slice of the ribcage,
where a large number of structures are located, such as
blood vessels, arteries, respiratory vessels, pulmonary
pleura and parenchyma, each with its own specific infor-
mation. Thus for analysis and for the pulmonary disease
diagnosis aid, it is necessary to segment the lung
Lung segmentation techniques have been developed
seeking optimization at this stage. Sluimer, Prokop and
Ginneken in [9], and Felix et al. in [6], achieved lung
segmentation of CT images using the region growing
method. Felix et al. in [10], Hu, Hoffman and Reinhart in
[11], and Silva et al. in [12], put together mathematical
morphology, region growing, thresholding and edge
detection methods to obtain even more accurate results.
The Pulmonary Emphysema Detection system (PEDS)
performs automatic lung segmentation through region
growing associated with binary morphological operations
of dilation and erosion [8].
Another methodology for lung segmentation is the 3D
region growing approach, and this is applied to segment
the lung and its internal structures, such as the vessels
and airways [13-16]. This method works similarly to the
2D region growing technique that is initialized by a seed
point and then expanded through its neighbors, obeying
an aggregation rule.
In addition, other computational tools have also been
used to evaluate lung structures, for example, Chen et al.
in [17], compared the diagnostic performances of an
artificial neural network (ANNs) and multivariable logis-
tic regression analyses for differentiating between malig-
nant and benign lung nodules on computed tomography
scans. Er, Yumusak and Temurtas in [18], also used
ANNs to analyze chest disease diagnoses by using mul-
tilayer, probabilistic, learning vector quantization, and
generalized regression neural networks. These authors
also used an artificial immune system classification [19],
and obtained high accuracy in chest disease diagnoses.
Fernández et al. in [20], evaluated serum biomarkers le-
vels in lung cancer patients and non-cancer controls us-
ing principal component analysis and ANNs modeling.
Active contour methods have also been widely re-
ported in the literature to segment lung structures from
CT images. Annangi et al. in [21], proposed a region-
based active contour method for x-ray lung segmentation
using prior shape and low level features. Wu, Wang and
Jia in [22], presented a novel external force, called adap-
tive diffusion flow (ADF), with adaptive diffusion stra-
tegies according to the characteristics of an image region
in the parametric active contour model framework. Ke-
shani et al. in [23], used a support vector machine com-
bined with an active contour model to segment lung no-
dules. Tan, Schwartz and Zhao in [24], combined image
processing techniques of marker-controlled watershed,
geometric active contours as well as the Markov random
field to evaluate lung lesions considering their size, den-
sity, and shape. Finally, Liu and Bovik in [25], proposed
a novel external force for active contours, which we call
neighborhood-extending and noise-smoothing gradient
vector flow to extract the parenchyma and cancer re-
This work contemplates two singularity methods, the
3D region growing and the multi-thresholding algorithms,
for the segmentation of the lung and its internal struc-
tures. The methods aim to assist medical diagnosis aid in
pulmonology diseases though 3D visualization and re-
construction from bi-dimensional CT images. After ima-
ge segmentation, it is possible to visualize and quantify
the lung structures, as well as its internal structures
tridimensionally. This facilitates the diagnosis by the
medical experts and reduces/eliminates the subjectivity
of interpretation of the test. The results obtained from the
proposed method that performs the bi-dimensional seg-
mentation and volumetric quantifying of all lung struc-
tures were evaluated and validated by two medical spe-
cialties (pulmonologists).
One of the main contributions of this work is to use
the proposed method on personal computers, maintaining,
or in some cases, improving the performance. Also the
use of a personal computer reduces the processing costs,
the use of the CT scanner workstation and reduces the
time to evaluate a test.
This section describes the digital image acquisition
through Computed Tomography (CT) and, afterwards,
the lung segmentation, reconstruction and visualization
method using the 3D region growing method.
Prior to the image acquisition, the tomograph is set to
an air density of 1000 HU (Hounsfield Units). The cali-
ation is carried out within three months of the exams, as
specified by the manufacturer [26]. Also before the
acquisition, a tomographic cut with a water phantom
with a known density is performed for analysis and para-
meter control of the system [2]. The images are quan-
tified in 16 bits and stored in a DICOM format (Digital
Imaging and Communications in Medicine).
To read these image files, which are in the DICOM
format, the free DICOM toolkit offered by OFFIS was
used. This library is compatible with the C++ program-
ming language, as used in this work.
Copyright © 2013 SciRes. OPEN ACCESS
P. P. Rebouças Filho et al. / J. Biomedical Science and Engineering 6 (2013) 1099-1108 1101
Figure 1(a) shows an example of an image obtained
using CT with a multi-detector. This image is based on
the principle that every internal structure of the human
body possesses a determined radiologic density value,
expressed in Hounsfield Units. The pulmonary densities
adopted in this work are: hyper aerated (1000 to 950
HU), normally aerated (950 to 50 HU), poorly aerated
(500 to 100 HU), not aerated (100 to 100 HU) and
bone region (600 to 2000 HU) [27,28]. The distribution
of these density bands is shown in Figure 1(b), in which
the red color represents the hyper aerated region, the
dark blue the normally aerated areas, the light blue
represents the poorly aerated areas, and the black the non
aerate areas, while the green represents the bone region
and the white the non-classified area.
2.1. 3D Pulmonary Visualization and
The graphic visualization system used in this work was
the Open Graphics Library (OpenGL) Application Pro-
gramming Interface (API). This API is an open and mul-
tiplatform specification of a graphical and modeling rou-
tine library, for the development of graphical computing
applications, such as games and visualization systems.
Two libraries that make up the OpenGL, GLU and
GLUT, both open sources were used in this work. The
OpenGL Utility Library contains various routines with
low level OpenGL commands to execute tasks such as
defining the matrix for the projection and orientation of
the visualization, and render a surface. The OpenGL Uti-
lity Toolkit (GLUT), which is a non-platform dependent
toolkit and includes some graphic interface elements was
also used here.
Thus the routines for the 3D development system of
this work were developed in C/C++ language, and called
for the OpenGL library routines. The GLU library is
applied to render objects, and configure their shape and
illumination. The GLUT library, was also used to create
windows and receive the user commands. This system is
capable of being visualized in various platforms.
In the case of specific CT images, the image spacing,
(a) (b)
Figure 1. CT lung image in the axial position: a) multi-detector
CT scanner, and b) representation of the density ranges.
or between planes, is one of the DICOM pattern proper-
ties, defined by the slice thickness property. The pixel
size is application specific when these images are 3D
processed and in the DICOM pattern, and this is deter-
mined by pixel spacing. Using these two parameters it is
possible to reconstruct the CT on the same scale it was
generated on.
2.2. Lung Segmentation Using 3D Region
Region growing is an image segmentation technique
used to unite the regions of interest. This technique
clusters the subgroups or groups of pixels in a deter-
mined region. This is possible through the expansion of a
given region initialized by a pixel, named seed. This
expansion occurs by the aggregation of pixels to the re-
gion through successive analysis iterations of the neigh-
borhood of a given region [29].
In this technique, for a pixel to be grouped to the
region, it is necessary for it to be in the region’s neigh-
borhood and to obey pre-established criteria. This gene-
rally bases itself on predefined parameters such as in-
tensity of gray tone, and gray tone mean in the region,
among others.
An illustration of the technique’s application is pre-
sented in Figure 2, in which the seed is shown in red,
Figure 2(a), and the clustering ruling is that the gray
tonality needs to be equal to the chosen seed’s tonality,
resulting in the shown segmentation in Figure 2(b).
The efficacy of this method depends directly on the
choice of the seed and the grouping rule. If these choices
are not adequate, the segmentation could present failures.
The 3D expansion of this technique is possible assuming
each element’s neighborhood is analyzed considering
nearby planes. This technique is common for objects and
medical image segmentations, principally in CT images.
A set of images from such exams follows a predeter-
mined sequence, obeying a standard pattern [13,16]. In
these cases the smallest element is the voxel and the lo-
cale begins being analyzed in the “x”, “y” and “z” axes.
An example of this evolution is presented in Figure 3.
(a) (b)
Figure 2. Illustration of the region growing method: a) original
image (seed represented by red color), and b) result of the
segmentation (red color).
Copyright © 2013 SciRes. OPEN ACCESS
P. P. Rebouças Filho et al. / J. Biomedical Science and Engineering 6 (2013) 1099-1108
The block diagram to execute the 3D region growing
method is presented in Figure 4, and its initialization at
the curve startup is responsible for the segmentation of
the regions of interest.
The algorithm flowchart is presented in Figure 4; the
first step of the method is the initialization with the seed.
In this work the initialization through the INAUTO, in
[8], method is employed, in which all images are analy-
zed and when two seeds are found in regions which con-
tain lungs (500 to 1000 HU), the position of the “z”
axis of these targets is stored. The mean “z” coordinate
among all previously stored targets is used as the ini-
tialization target and the points found through the
method are the seeds of the 3D region growing method.
Figure 5 shows the 3D RG Steps.
From the initialization the region grows by successive
iterations, calculating the area before, analyzing and
aggregating the predetermined neighboring region. At
the end of every iteration, the area is recalculated. The
chosen method of analysis and the clustering of neigh-
boring regions utilize the pulmonary anatomy informa-
tion, adding voxels that are in some intensity band within
the lung, which are: normally, poorly or hyper aerated
(500 to 1000 HU). This adding is made by successive
(a) (b) (c)
Figure 3. Illustration of the region growing method: a) voxel
determination (seed in red color), b) first iteration analyzing
neighborhood of the seed voxel, and c) result of the segmenta-
Figure 4. Block diagram to apply 3D region growing.
Figure 5. Illustration of the 3D region growing evolution (blue
color) for the lung segmentation from CT of the thorax: initia-
lization, evolution of the 3D region growing, and result of the
iterations until stabilization when no voxel is aggregated
to the region.
2.3. Thresholding and Multi-Thresholding
This segmentation technique is based on thresholding. Its
basic principle is to determine a value as a threshold,
generally in a gray tone that is within the range of tones
used in the image. For example, in an image with an 8 bit
resolution, the threshold may be between 0 and 255.
After establishing the threshold of all the regions in the
image, it is possible to label every pixel, associating it to
the value band established in each region. When there
are just two regions for classification, one of these re-
ceives the label 0 and the other 1, and in this case the
technique is called binarization. More than one threshold
can be established in the same image; this technique is
called multi-thresholding [30,31]. This technique subdi-
vides the image in more than two regions, establishing
the lower and the higher limits of each region of interest.
The multi-thresholding was used in this work with
pulmonary anatomy data, in which every human tissue
obeys a specific density bracket. The main density
brackets being: 1) hyper aerated (1000 to 950 HU), 2)
normally aerated (950 to 500 HU), 3) poorly aerated
(500 to 100 HU), 4) not aerated (100 to 100 HU),
and, finally, 5) bone (600 to 2000 HU).
Using these radiological density brackets, it is possible
to detect some structures and diseases in the lungs such
as emphysema, vessels, airways and bones. From these,
bones can be detected and located without the lung seg-
mentation, while the rest of the structures are detected
and located after the lung segmentation.
2.4. Qualitative Evaluation Measurements
The evaluation measures adopted in this work are based
on the qualitative approach of the segmentation as pro-
posed by Gonzales and Woods in [29]. Thus, there are 5
classification criteria considered in the evaluation criteria:
optimal, acceptable, reasonable, bad and worst. This
classification and its respective grades are described in
Table 1.
This classification, presented in Table 1, was used by
Rebousas Filho in [32], in the evaluation of lung seg-
mentations of CT images. Also, Cavalcante in [33], eva-
luated the segmentation of airways by this qualitative
evaluation method.
2.5 Inter and Intra Observer Evaluation
The index of inter and intra concordance constitutes a
measure used to analyze the agreement between two
observation intervals (intra observers) and between eva-
luators (inter observers) in the attribution of categories
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P. P. Rebouças Filho et al. / J. Biomedical Science and Engineering 6 (2013) 1099-1108 1103
for a determined classification, as shown in Table 1.
The statistical concordance measurement used in this
work is the Cohen’s Kappa index. This index is an intra
and inter observer concordance measurement and meas-
ures the degree of concordance between two observers
and varies from 0 to 1, in what 1 means a greater con-
cordance and 0 suggests the concordance is attributed
randomly [34]. The Kappa index is calculated from a
confusion matrix, an example of this is presented in Ta-
ble 2.
The Kappa analysis is a discrete multivariable used in
a thematic precision evaluation and uses all the elements
of the confusion matrix. The Kappa coefficient (K) is a
measurement of the real concordance, indicated by the
diagonal confusion matrix elements, minus the concor-
dance by chance, indicated by the total line versus the
column result, which does not include non-recognized
inputs. This means it is a measure of how much the
classification is in accordance with the reference data.
Further details on the calculation of the kappa index can
be seen in [35].
Although the Kappa coefficient is quite often used to
evaluate the exactness of mapping, there are no minimal
acceptable levels of this coefficient in a classification
[35]. However, Table 3 presents classification perfor-
mance levels, normally accepted by the scientific com-
Nowadays, the Kappa index is largely used to calcu-
late the concordance level between observers in various
applications such as, classification and image analysis of
Synthetic Aperture Radar [36], classification of subcel-
lular molecules [37], classification of breast lesions [38]
and the evaluation of multimedia quality [39].
Table 1. Evaluative criteria for qualitative image segmentation.
Note Classification Description
5 Optimal High quality: as good as might be desired
4 Acceptable Good quality with minor errors
3 Reasonable Intermediate quality with outliers
2 Bad Only a small portion of the object of interest
1 Worst Does not segment the object of interest
Table 2. Illustration of a confusion matrix [35].
Classification 1 2 c Line total ni+
1 X11 X
12 X
1c X
2 X21 X
22 X
2c X
c Xc1 X
c2 X
cc X
Line total n+i X
+1 X
+2 X
+c n
In this section, the method of medical image acquisition
is presented first. Then, the segmentation using the 3D
region growing is evaluated on CT lung images of the
thorax of healthy patients and patients with COPD and
fibrosis. Finally, some applications are presented to pro-
ve the robustness, effectiveness and efficacy of the pro-
posed method.
3.1. Acquisition of the Medical Images
The images used to evaluate the algorithms were ac-
quired from different CT scanners, some of the images
were saved as samples of a complete CT exam, while
other images were saved together with the complete
The models used to acquire the complete exams were
the Toshiba Aquilion (TA), the GE Medical system
LightSpeed 16 (GEMSL) and the Phillips Brilliance 10
(PB). All images were of 512 × 512 resolution and 16
bits. Table 4 gives the characteristics of these complete
Table 3. Kappa’s index and classification performance [34].
Kappa index (K) Performance
0 No concordance
0 to 0.20 Slightly
0.21 to 0.40 Reasonable
0.41 to 0.60 Moderated
0.61 to 0.80 Substantial
0.81 to 1 Excellent (full concordance)
Table 4. Description of full exam used to evaluate the 3D
N˚ of examN˚ of imagesSlice thickness Model of the
tomography Disease
1 908 0.5 mm TA Normal
2 297 0.5 mm TA Normal
3 685 0.5 mm TA Normal
4 760 0.5 mm TA Normal
5 229 3.0 mm TA COPD
6 278 1.25 mm GEMSB Normal
7 267 1.25 mm GEMSB Fibrosis
8 239 1.25 mm GEMSB Normal
9 276 2.0 mm PB Fibrosis
10 296 2.0 mm PB Normal
11 597 1.0 mm PB COPD
Copyright © 2013 SciRes. OPEN ACCESS
P. P. Rebouças Filho et al. / J. Biomedical Science and Engineering 6 (2013) 1099-1108
exams, conceded by a pulmonologist, with the patient’s
These images constitute an image cluster obtained in
partnership with the Walter Cantídio Hospital of the
Federal University of Ceará, submitted to an earlier study
[1,2]. This study was approved and evaluated by the
UFC Research Ethics Committee—COMEPE (Protocol
n˚ 35/06) and complied with the demands of Resolution
n˚ 196/96 of the National Health Council, concerning
research in human beings.
3.2. 3D Region Growing
In the tests 11 complete thorax CT exams were used. The
exams were taken of healthy patients and patients with
COPD and fibrosis. The segmentation results using the
3D region growing are shown in Figure 6.
A complete thorax CT exam has between among 250
and 1000 images, therefore the results from the 3D re-
gion growing method must be evaluated as the evalua-
tion of every single image by a pulmonologist is unvia
The sampling used in this work is based on the human
lung, and is divided into four distinct regions (Figure 7)
such as 1, 2, 3 and 4 corresponding to the superior lobes,
the hilar region, the base and the diaphragmatic region,
The group of images for analysis is constructed using
3 CT images of each region, totaling 12 images per exam.
Since 11 complete exams were used, there were 132
Figure 6. Illustration of the lung segmentation from CT exam
using 3D region growing with different thickness.
Regiom of the upper lobes
Hilar region
Base region
Diaphagmatic region
Figure 7. Lung regions considered during image selection for
each exam. Qualitative analyses of segmentation methods.
images. These images were used in the evaluation, by the
medical experts, of the CT exam lung segmentations,
which means that 264 lungs have to be evaluated. This
analysis is realized using qualitative metrics due to the
impossibility of using quantitative metrics. A quanti-
tative analysis would be unviable as the manual segmen-
tation of the images by a specialist physician is extre-
mely slow and tiring, considering a total of 132 images.
Thus, the qualitative evaluation used in the segmen-
tation of the lungs by the 3D region growing method
consists in attributing grades to the segmentation ob-
tained by the method. In this work, two pulmonologists
were responsible for this evaluation. The attributed
grades in this evaluation possessed values from 1 to 5, in
which the respective Qualitative Evaluation Criteria
(QEC) of the segmentation is from worst, bad, reasona-
ble, acceptable to optimal. The results are considered
satisfactory when they are optimal or acceptable, which
represents little to no error in the results.
The physicians who evaluated the results of the
methods in question are professors and pulmonologists at
the Walter Cantídio Hospital, and were nominated as
physician 1 (P1) and physician 2 (P2). The physicians’
evaluations, for each classification performance, were
presented in Table 3.
The Kappa index value, obtained in the inter observer
concordance is 0.684, indicating substantial concordance
between the physicians concerning the 3D region grow-
ing algorithm.
The indexes obtained in Table 5 show the quality of
the segmentation results, and the physicians 1 and 2 con-
sidered 64.78% and 68.18%, respectively, of the results
satisfactory, having little or no errors.
However the two physicians (1 and 2) identified
serious errors in the results obtained by the 3D region
growing method in 29.16% and 28.55%, respectively,
and these were classified in group 3 of Table 5. Bad
segmentations, classified as class 2, were identified by
the two physicians in 6.06% and 2.27% of the results
obtained by the 3D region growing method.
Table 5. Qualitative evaluation of the specialist P1 and P2.
3D region growing
Classification P1 [%] P2 [%]
1 0.00 0.00
2 6.06 2.27
3 29.16 29.55
4 48.11 50.00
5 16.67 18.18
Satisfactory 64.78 68.18
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P. P. Rebouças Filho et al. / J. Biomedical Science and Engineering 6 (2013) 1099-1108 1105
Some examples of the results obtained by the evalua-
ted method are presented in Figures 8 and 9. These fig-
ures present the main result types obtained from the phy-
sicians’ evaluation. Figure 8 presents an illustration of
an evaluation type 5 (Table 1 ), for both lungs according
to both physicians.
The most frequent error present in the segmentation of
the CT images that the physicians disagreed upon is
shown in Figure 9, where the presence of a lung disease,
in this case, fibrosis stands out.
3.3. Applications for Medical Lung Diagnosis aid
Among the research areas focusing on medical diagnosis
aid, the pulmonology area stands out, as various diseases
can be detected and tracked using pulmonary CT images.
This is possible due to the development of software
created for this purpose, in which the lungs present in
thorax CT images are extracted and, afterwards, analyzed
through Pattern Recognition and Artificial Intelligence
algorithms for the detection of diseases and the internal
structures of the lung.
3.4. 3D Reconstruction Visualization by Cuts
The 3D reconstruction of the lung obtained by a com-
plete thorax CT exam enables the visualization of the
lung anatomy in 3D. Auxiliary specific cut planes can be
inserted in the 3D visualization to aid the specialized
physician. This method of visualization through a model
and its cut planes increases the accuracy of the physi-
cian’s diagnosis, as the amount of information for the
diagnosis is increased.
To exemplify these planes, Figure 10 presents a 3D
model with the axial, sagittal and coronal planes aiding
the visualization of the lungs in thorax CT images. It
(a) (b) (c) (d)
Figure 8. Illustration of the lung segmentation from CT image
analyzed by two specialists as optimum (Note 5—Table 1): a)
and c) original images, and b) and d) results using 3D region
(a) (b) (c) (d)
Figure 9. Illustration of the lung segmentation from CT image
with fibrosis: a) and c) original images. The lung regions with
fibrosis were not considered when the 3D region growing
method was applied, b) and d).
should be noted that this 3D model was obtained through
the reconstruction of a CT exam only using axial im-
agery. The other cut planes are obtained through the re-
constructed 3D model.
3.5. Analysis and Detection of Airways
Airways are characterized by their hyper aerated bracket
and their specific anatomy. They can be detected using
the 3D region growing method, and the seed is the most
elevated voxel in the lung segmentation and the ruling
for neighborhood aggregation is to be in the hyper aer-
ated region. Figure 11(a) shows an example of segmen-
tation of the airway and Figure 11(b) shows the lung
segmented without the airways.
3.6. Analysis and Detection of Emphysema
An example of a disease that can be detected by this
technique is pulmonary emphysema. This disease is cha-
racterized with a density between 950 and 1000
Hounsfield Units, and thus is considered as a hyper aera-
ted region.
This technique can be applied to only one image or to
a complete thorax CT exam. Figure 12 presents an
image of an emphysema in 3D using the multi-threshol-
ding method.
3.7. Detection of Pulmonary Vessels
The blood vessels inside the lung are characterized by
their radiological density of between 100 and 500
Hounsfield Units, which is considered a poorly aerated
region. An example of the detection of these inside the
lung is shown in Figure 13, in which the vessels can be
(a) (b) (c) (d)
Figure 10. 3D analysis: a) 3D visualization and reconstruction,
and cutting planes b) axial, c) sagittal and d) coronal.
(a) (b)
Figure 11. Segmentation of airways through 3D region
Copyright © 2013 SciRes. OPEN ACCESS
P. P. Rebouças Filho et al. / J. Biomedical Science and Engineering 6 (2013) 1099-1108
presented individually or inside the lungs.
3.8. Bone Region Detection and Analysis
The bone region is characterized as being in the 600 to
2000 HU bracket, and can therefore be detected through
a threshold using the cited band. Figure 14 presents an
example of the result of multi-thresholding applied to the
bone region.
The general aim of this work, which was to show the
segmentation, reconstruction and visualization of lungs
in thorax CT images using the 3D region growing
method, was achieved.
The results obtained using healthy patients and patien-
ts with fibrosis and COPD were analyzed together by
Figure 12. Full exam showing 3D detection model of a hyper-
inflated region.
Figure 13. 3D visualization of the blood vessels.
(a) (b)
Figure 14. 3D reconstruction: a) bone region and b) lung and
bone region.
two pulmonologists, and 45.83% and 68.18% of satisfac-
tory results for physicians P1 and P2 were obtained,
respectively. The concordance between the two physi-
cians by the Kappa index showed a moderate agreement.
A future work will aim to improve the results of the
patients with COPD and fibrosis, as the segmentations of
healthy patients achieved good results, which were not
the cases with some of the other patients. These less
good results are because the diseases alter the radiologi-
cal density brackets in HU. One of the ways to correct
this will be to insert Artificial Intelligence in the cluster-
ing of the vicinity neighbors.
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