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AGGREGATED MODEL FOR TUMOR IDENTIFICATION
AND 3D RECONSTRUCTION OF LUNG USING CT-SCAN
Syed Abbas Ali
N.E.D University of Engineering and Technology, Karachi, (Pakistan).
E-mail: saaj@neduet.edu.pk ORCID: https://orcid.org/0000-0001-6014-1559
Nazish Tariq
Dow University of Health and Sciences, Karachi, (Pakistan).
E-mail: nazish.tariq1988@gmail.com ORCID: https://orcid.org/0000-0002-3275-390X
Sallar Khan
Sir Syed University of Engineering and Technology, Karachi, (Pakistan).
E-mail: sallarkhan_92@yahoo.com ORCID: https://orcid.org/0000-0001-8988-3388
Asif Raza
Sir Syed University of Engineering and Technology, Karachi, (Pakistan).
E-mail: asif.raza@ssuet.edu.pk ORCID: https://orcid.org/0000-0002-2340-0627
Syed Muhammad Faza-ul-Karim
Sir Syed University of Engineering and Technology, Karachi, (Pakistan).
E-mail: fkarim15@yahoo.com ORCID: https://orcid.org/0000-0002-9039-8184
Muhammad Rahil Usman
Sir Syed University of Engineering and Technology, Karachi, (Pakistan).
E-mail: usman.rahil@gmail.com ORCID: https://orcid.org/0000-0002-8385-3345
Recepción:
09/01/2020
Aceptación:
09/04/2020
Publicación:
30/04/2020
Citación sugerida Suggested citation
Ali, S. A., Tariq, N., Khan, S., Raza, A., Faza-ul-Karim, S. M., y Usman, M. R. (2020). Aggregated
model for tumor identication and 3D reconstruction of lung using CT-Scan. 3C Tecnología. Glosas de
innovación aplicadas a la pyme. Edición Especial, Abril 2020, 159-179. http://doi.org/10.17993/3ctecno.2020.
specialissue5.159-179
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ABSTRACT
This paper facilitates radiologists in diagnosis of lung tumor and provides with a
probability to dierentiate between the types of tumor through automated analysis and
increase in accuracy. The system is aggregated model for tumor identication and 3D
reconstruction of lung using (computed Tomography) CT-scan images in Digital Imaging
and Communications in Medicine (DICOM) format to identify the lung tumor (Benign or
Malignant) using learning algorithm. The proposed system is capable to reconstruct the
3D model of lung tumor using CT-scan medical images and identify tumor (Benign or
Malignant) including location of tumor (Attached to wall or parenchyma) with signicant
accuracy. The proposed diagnostic software provides signicant results with bright CT scans
to identify lungs tissue with dierent orientations by rotating it and reduces the enormous
false positive rate by increasing the eciency and accuracy of the diagnostic procedure.
Whereas, CT-scan image is below required brightness or if CT-scan is done in a dark
room than the module does not shows considerable results of segmentation. The proposed
computer aided diagnosis can help the radiologists to detect tumor at early stage, decrease
the enormous false positive rate, and the overall cost of the diagnostic procedure; thus,
bringing windfall benets in the eld of medical imaging.
KEYWORDS
Medical Imaging, Image Processing, Lung Tumor, Machine learning.
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1. INTRODUCTION AND RELATED WORK
Health of an individual is a worldwide issue that implies lots of problems especially where
health service may be decient and unsupervised. Lungs tumor is one of the leading causes
of death in both males and females. Smoking is one of risk factors of lung tumor. However,
the duration of smoking and number of cigarettes consumed also contribute a lot in the
risk of lung tumor. The chances of expanding lung tumor can be lowered if the habit of
smoking is dropped to a certain extent. Majorly, both smokers and people are exposed
to second-hand smoke. Individuals who were never passive smoker and never exposed to
second-hand smoke can have tumor. There might be no apparent cause of lungs tumor
in these cases. Doctors believed that lungs tumor damage the cells; when an individual
inhales smoke of the cigarette, which is full of substances (carcinogens) that can cause
tumor, immediately bring changes in lungs tissue.
Firstly, the body may be able to adjust these changes, but with the continuous exposure,
the normal cells that link lungs are damaged increasingly. Afterwards, the damaged cells
act abnormally and develop tumor eventually. The occurrence of small lungs tumor is
exclusively found in heavy smokers. Small lungs tumor is least common as compared
to non-small lungs tumor. Adenocarcinoma, large cell carcinoma, and squamous cell
carcinoma are included within on-small cell lung tumors. Scientists currently developing
a diagnostic system based on sputum color images. Many algorithms for medical imaging
have been suggested. In lung tumor detection method, a robust method of abstraction
was proposed in which all the features are transferred through an articial neural network
(ANN) accompanied by training the framework for classication purposes (Miah & Yousuf,
2015).
Previously, an edge system was used to recreate volumes of emphysema in the lungs
(McCollough et al., 2006). A limit method was appropriate for dividing each picture.
However, a 3D reproduction was not endeavored. The normality or abnormality of the
lung is determined by image processing-based identication of lung tumor on CT scanning
images. Lung tumor has been optimized through support vector machine (SVM) algorithms
and techniques (Abdillah, Bustamam, & Sarwinda, 2017). In this optimization, the function
used is relied on gradient-oriented histogram, color moments, texture, and; therefore, is
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graded for identifying its medicinal value (Venkataraman & Mangayarkarasi, 2017). A
vector-dependent support method was used based on the gray level rivalry matrix and the
eight texture characteristics histogram to describe and dierentiate objects either without
or with nodules (Madero et al., 2015).
No thought was given during the stage of process segmentation. Similarly, lung CT images
testing were performed and indicated eective use of predictive computer-aided design
(CAD) for lung tumor diagnosis (Tiwari, 2016; Ritika et al., 2011). An enhanced SVM
classier was used for diagnosing leukemia tumor using a fast-correlation-based lter to
choose the most inuential and non-correlated genes. The picture was transformed to the
processing period and provided more accurate results. Lung tumor detection phases in CT
scanning pictures use dierent image processing techniques (Kavitha, Gopinath, & Gopi,
2017).
Picture quality appraisal changes where low pre-preparing strategies were utilized
considering Gabor channel inside Gaussian guidelines. Depending on general highlights, a
typical correlation was made. In this exploration, the primary identied highlights for exact
pictures correlation were pixels rate and mask-labeling (Altarawneh, 2012). Lung tumor
detected using the ANN has also low accuracy. It was comparatively easier for abstract or
complex issues such as image identication but increase precision to strengthen the scale by
several extents (Agarwal, Shankhadhar, & Sagar, 2015; AlZubaidi et al., 2017).
The noteworthy change was noted in previous study conversely of masses alongside the
concealment of foundation tissues, which were acquired by tuning the parameters of the
proposed change work in a predened extent (Patil & Kuchanur, 2012). A study was on lung
tumor detection using a deep learning approach. A pipeline of pre-processing techniques
has been proposed for emphasizing lung regions susceptible for tumor and extracted
features through ResNet and UNet models. The element set was assigned into dierent
classiers through Random Forest and XGBoost. Methods for detecting lung tumor nodule
were evaluated, including principal component analysis, support vector machines, Naïve
Bayes, decision trees, and articial neural networks, and K-Nearest neighbors. The study
has compared all strategies for pre-processing and without pre-processing. According
to the results, the best outcome was obtained from the ANN with approximately 82%
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accuracy after image processing. CT images of lungs were examined on a computer-aided
classication (Dev et al., 2019).
MATLAB software has been applied to implement all the procedures in the proposed
system. Dierent stages entailed image pre-processing, segmentation, SVM classication,
feature extraction, and image acquisition. Firstly, a threshold value was computed, and
image was segmented into right and left lung. Secondly, the DICOM format lung CT
image was surpassed as input, which undergone pre-processing. Lungs were extracted and
passed as input to the SVM after those 33 features of each segmented lungs. Lastly, the
CT image was categorized as tumorous or non-tumorous based on the training data. This
strategy allows more satisfactory outcomes when compared to other current systems (Dev et
al., 2019). Dierent computer-aided techniques were assessed for investigating the existing
technique and for examining their drawbacks and restrictions. In addition, the study has
proposed the new model with enhancements in the eective model. Lung tumor detection
techniques were listed and sorted based on their detection accuracy.
The techniques were investigated on each step and overall restriction, whereas limitations
were pointed out. Some techniques might have higher accuracy and low accuracy, but
not closer to 100%. The need of 3D rotatable model was to view lungs from dierent
orientations and other functionalities for detecting lungs tumor including feature extraction
nodule detection by learning classier, active contour-based nodule contour extraction, and
nodule connectivity recognition by tissue classication. Detection of tumor using learning
algorithm support vector machine (SVM) helps radiologist to diagnose the nature of tumor
by performing biopsy. The proposed diagnostic tool detects and classies lungs tumor as
benign or malignant.
Detection was based on extracted features, in comparison with benign and malignant sizes.
The objective was to see lungs tissue with dierent orientations by rotating it and reducing
the enormous false positive rate by increasing the eciency and accuracy of the diagnostic
procedure. It not only reduces the overall cost but also creates a better environment for the
screening of lung tumor; lessens the duration of diagnosis, and prognosis of the disease.
The signicance of this paper is of twofold. Firstly, this study aims to mitigate the variability
in order to assess and report the lung tumor risk between interpreting physicians. In fact,
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computer assisted approaches have been observed for enhancing consistency between
physicians in dierent clinical contexts such as mammography screening and nodule
detection. Secondly, learning classier can enhance classication performance by stimulating
the less experienced or non-experienced clinicians in order to evaluate the risk of a specic
nodule being malignant.
2. DESIGN PHASES
This paper has divided its objective in three dierent phases.
Firstly, this study extracted or selected dierent features for the 3D construction. Secondly,
this study has developed learning classier. Lastly, the study has designed a tool for integrated
solution. In the rst phase, personnel from two dierent areas of expertise took part. Firstly,
radiologists help to understand the relative information of the anatomy, physiology of
the lung functionality and the symptoms of the tumors, and their types derived from the
available CT scans.
Secondly, researchers from engineering discipline analyzed the transformation of the
parameters related to lung tumors (disease and their types) in computer understandable
parameters, which were used to drive the further stages for developing the tool. The second
phase of this paper processed the related information that was transformed from physical
results of patients to computer based learning algorithms.
In this paper, one of the signicant research oriented tasks was to identify the best or fairly
relative feature to transform the actual information of the CT scan result into computer
based outcome. Machine learning was used to achieve this goal and the selection of the
feature was done from available feature stream and some were derived as new features using
in-depth understanding of the physiology of the lung with and without disease. In the next
step, the machine learning tool used the derived features from the previous step to select the
most appropriate or accurate classier based on recognition percentage value. These results
were used to develop the combination of dierent classiers with derived features to select
the most accurate classiers in particular situation. The result of the classier identied
lung tumor with appropriate features and learning classiers.
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In this phase, the CT scan images were segmented to get the mask of images by applying
multiple functions of image processing. The masks of these images were combined to get
the 3D image, which can be seen over dierent orientations using Matlab viewer. The
lung tissue was used fully for radiologists to identify the tumor by just looking at the 3D
reconstructed images. In the third phase of this paper, an integrated application for lung
tumor detection was developed.
The results derived were used along with signicant features, classiers, and their
combinations. The developed software tool for lung tumor detection was capable of
addressing the following aspects:
• Improve the false positive rate of results.
• Software based CT scan Diagnosis.
2.1. FEATURE SELECTION
The following parameters for lung tumor classication, symptoms and nodule classication
were Apex, Base, Lobes, surfaces and borders:
Lobes: Lobular structure of both the lungs varies from each other. The left lung comprises
of inferior and superior lobes, which were divided by an oblique slit. The right lung was
comprised of middle, inferior, and superior lobes. In addition, the lobes were divided from
each other by two dierent ssures.
• Oblique ssure – starting from the inferior border, it grows in the super posterior
direction, till it sees the posterior lung border.
• Horizontal ssure: It increases horizontally at the 4
th
rib level for meeting the oblique
ssure from the sternum.
Lung tumor may be caused due to the irregular and uncontrollable growth of cells in lung
tissue. Lung nodules were the abnormalities in the lung tissue. They were generally small
and approximately spherical masses of tissue, having a size of about 5 millimeters to 30
millimeters. Non-small cell lung tumor and small cell lung tumor were two broad categories
of lung tumor.
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Hemoptysis, weight loss, shortness or wheezing of breath, fatigue, fever, and coughing
were the signs and symptoms of lung tumor. Due to a tumor mass, the symptoms stress
on adjacent structures such as superior vena cava obstruction, diculty swallowing, chest
pain, and bone pain. The presence of metastatic disease was recommended within the
symptoms, including weight loss, neurological symptoms, and bone pain. The kidney, bone,
brain, adrenal glands, liver, pericardium, and opposite lung were included as common sites
of spread. There were two major classications of a tumor. It can either be a benign or a
malignant tumor with the following characteristics as shown in Figure 1 and Figure 2.
2.1.1. BENIGN CHARACTERISTICS
• It was not tumorous.
• It was localized in a region and does not invade in other tissues.
• The size was less than 2 cm and does not changes for 2 years.
• It was rounded in shape with smooth edges.
• It has calcium deposits in it and the HU value was near to bone.
2.1.2. MALIGNANT CHARACTERISTICS
• It was tumorous
• It invades in surrounding tissues and may spread in the body.
• Size was more than 2.5cm.
• It has irregular and speculated edges.
• It has no calcium deposits and HU values corresponds to that of uids and soft tissue
2.2. SELECTED FEATURES
The following features were selected for classication of tumor area, perimeter, diameter,
centroid, robustness, smoothness, indentation, and calcication along with pixel value for
tumor classication.
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Figure 1. Benign Pulmonary Nodule with smooth edge and central calcication.
Figure 2. Malignant Pulmonary Nodule with speculated edge and ipsilateral deposits.
2.3. FEATURE EXTRACTION
Feature extraction was used to extract the lung mask from CT-scan images. Dataset was
generated using 16 slice machine currently being used in many hospitals in Pakistan. It
included many variations in cases. Some were squamous cell carcinoma, adenocarcinoma,
and large cell carcinoma. The study has used DICOM image format using 3D Viewers
to extract DICOM image features. Segmentation module was used after preprocessing to
extract lung mask from CT-scan images in Figure 3. There were dierent set of tools used
to develop the software for 3D reconstruction and identication of lungs tumor. MALAB
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tool was used for Graphical User Interface (GUI), image segmentation for getting 3D
functionalities, and identication of wall tumor. Figure 4 shows the basic ow of system.
In 3D image viewer, function can be used to view 3D image volumes like CT scans. It has
maximum Volume Rendering (VR), Slice render or intensity projections (MIP) (Figure 5).
Image Segmentation for getting 3D mask functionalities comprises on following steps as
shown in Figure 6.
Figure 3. Lung Mask.
2.4. LEARNING CLASSIFIERS
Machine learning oers systems the competence for getting more appropriate outcome to
predict result regardless being comprehensively programmed. Data mining and predictive
modeling was the process involved in machine learning. Support Vector machine was an
attractive approach for dening decision boundaries, as the idea related about decision
planes. Simple technique for nding decision planes that a set of objects separates out
between dierent memberships of class. SVM was used for classifying data analysis and
regression analysis. For example, there was a n-dimensional space; the study has plotted
each data item in each point that each feature value being the particular coordinate value.
By nding the best hyper-plane, the study has performed the classication that dierentiates
the two classes very well.
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Figure 4. Design Flow Diagram.
3. EXTRACTION OF SLICES AND 3D RECONSTRUCTION
In this module, the tumor was extracted from lungs mask and converted them into object
form (Array). The array obtained consists of dierent ranges of pixels. The number of
pixels varies with the CT scan. Some CT scans may have 250 pixels; some may have more
than 400 pixels. Dierent set of tools were used to develop the proposed software for 3D
reconstruction and identication of lungs tumor.
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Calibration
Start
Measured
weight to high
Measured
weight to low
Zero scale with current
tare weight
Yes
Ask user to place
calibration weight on scale
Measure calibration
weight
Yes
Decrease calibration
factor
Increase calibration
factor
Calibration completed
PV module
Logging interface
Load
LabVIEW
Extraction of Slices
Image Viewer
Image Segmentation for
getting 3D mask
functionalities
Feature extraction and
classification
Figure 5. System Flow.
Extraction of slices were performed using 3D viewer. This function was used to view 3D
image volumes like CT scans. It has a maximum Volume Rendering (VR), Slice render or
intensity projections (MIP). In this window, ‘File’ has an option for loading medical data,
‘Render’ has an option viewing 3D slices,‘Volume1’ indicates that data was loaded and
‘save picture’ was used for saving slices coronal (XY), sagittal (YZ) and axial (XZ) (Figure 6).
Figure 6. Basic ow of 3D Viewer for extraction of slices.
4. IMAGE SEGMENTATION AND THRESHOLDING
Image segmentation divides the data into adjoining regions elected by individual anatomical
objects. The process of image thresholding was to divide an image into a background and a
foreground. Objects were isolated from image segmentation and gray scale images to binary
images. One of the most inuential aspects in images was image thresholding with high
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contrast levels. ‘Load’ option was used for load slices image that applies thresholding, moves
the ‘Threshold slider’ to adjust the segmentation until the lungs appear well-delineated, and
simply sets the ‘minimum’ and ‘maximum’ properties. The ‘save’ option was used for saving
thresholding slices images individually as shown in Figure 7.
Figure 7. Image Threshold Window.
Image viewer window was used for viewing each slice individually. The following steps were
developed to 3D reconstruction of lungs using mask shown in Figure 8.
Calibration
Start
Measured
weight to high
Measured
weight to low
Zero scale with current
tare weight
Yes
Ask user to place
calibration weight on scale
Measure calibration
weight
Yes
Decrease calibration
factor
Increase calibration
factor
Calibration completed
PV module
Logging interface
Load
LabVIEW
Extraction of Slices
Image Viewer
Image Segmentation for
getting 3D mask
functionalities
Feature extraction and
classification
Input: Extraction of Slices
Image Threshold
Invert Mask
Clear Border
Fill Holes
Extract Object
Figure 8. 3D Reconstruction of Lungs Using Mask.
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4.1. INVERT MASK
On the segmentation tab, click ‘Invert mask’ to make the segmented lungs the foreground.
‘Imcomplement()’ unction was the complement of binary image; black become white and
white become black; zeros and ones were reversed.
Figure 9. Invert Mask on Image Segmentation Window.
4.2. CLEAR BORDER
On the segmentation tab, click ‘Clear border’ removed all the segmented parts that were
not the lungs. Since these all touch the edges, use the ‘imclearborder()’ function to
remove them.
Figure 10. Clear Border on Image Segmentation Window.
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4.3. FILL HOLES
On the segmentation tab, click ‘Fill holes’ means ll the small holes that appear in the lung
areas. Use ‘imll(t,’holes’)’ function means input binary image lled with holes.
4.4. EXTRACT OBJECT
On the segmentation tab, click ‘Extract objects’ mean extract objects from binary image
by size. Use ‘bwarealt(t1,n,keep)’ function means it keeps the ‘n’ largest/smallest objects.
If you want to keep n largest object, specify the ‘keep’ parameter with the value ‘largest’.
Figure 11. Extract Objects on Image Segmentation Window.
4.5. JPEG, TIFF IMAGE SEGMENTATION
In this module, a JPEG or ti image was loaded and checked whether it was a grey scale
image or not. If not than it was converted into a grey image. Afterwards, the grey image was
used as input to draw the histogram of the pixels value in the image. The image was then
binaries and ll holes and clear border function was applied to lter the image. A function
to extract two lobes of lungs was called and extracted the lungs from the CT scan. Finally
the lungs mask was obtained by inverting the lungs using binary image.
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Figure 12. Tiff Image Segmentation.
Figure 13. Image Segmentation.
5. RESULTS AND DISCUSSION
To detect the tumor, proposed software methodology was tested on two fronts. Due to the
limited amount of data availability, the core focus was on a single image testing. The array
obtained from the previous module contains the length of pixel in mm. This data was used
to train the SVM algorithm and classied the data into two hyper planes. First, hyper plane
was assigned 1 for which the length of the pixel was below 30mm and the other hyper plane
consist of the pixels having length more than 30mm and they were assigned as -1. Since the
size for malignant tumor was more than 30mm, so basically this algorithm was based on
classifying malignant and benign tumor as shown in Figure 14.
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Figure 14. Position of pixel for more than 30mm.
All three modules were tested on single slice data of CT scan. The slice selected for input
was on random basis and was manually selected for gaining accurate results. Tumor on
selected slice was clearly visible so that the results can be compared, and the accuracy of
the module can be found. Two image testing formats were used in this proposed software
1) TIFF format and 2) JPEG format. The input was provided in TIFF format to module
and applied segmentation on image, TIFF images having less count than JPEG image. In
the output stage, considering JPEG image which were much clear than TIFF image. In the
nal phase of the system, the extracted tumors were then passed to a machine learning
algorithm. The algorithm compares the size of extracted tumor to the learned size and if
the size of some pixels exceeds a particular value then malignant tumor was identied. The
study has compared the results with the radiologists. If the malignant probability was more
for a case of malignant that means that system was working correctly. The study had found
no false negative rate in the system.
This study has performed image processing on CT scan image so the quality of image
was critical. Experimental output provided signicant results with bright CT scans. CT
scan image was below required brightness or if CT scan was done in a dark room than the
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module cannot do segmentation clearly. Accuracy of machine learning algorithm depends
on the data set. The more the data set, the more accurate results will come.
The system’s accuracy was currently compromised due to limited data. The accuracy can
be improved by training and testing the data on larger amount of data. Furthermore, some
other more advanced machine leaning methods can also be used for better performance.
Currently, the study was working on supervised learning to switch to unsupervised
algorithm, which may prove to be better for the accuracy of the system. The algorithm
can be implemented to other imaging techniques like PET scan, MRI, Nuclear Medicine,
bioluminescence, mammography, uorescence etc. so that one can see which technique
works well with computer aided diagnosis of lung tumor detection.
6. CONCLUSION
In this study, computer aided diagnosis software was proposed that can detect and classify
lungs tumor as benign or malignant. Detection was done based on extracted features,
in comparison with benign and malignant sizes. This proposed method incorporated
functionalities like segmentation of lungs through threshold and some morphology functions
to obtain the mask of lungs and then use these masks to make a 3D model. The need of 3D
rotatable model was to view lungs from dierent orientations and other functionalities for
detecting lungs tumor are feature extraction nodule detection by learning classier (SVM),
Active Contour Based Nodule Contour Extraction and Nodule Connectivity Recognition
by Tissue Classication. The diagnostic tool was capable for reconstructing the 3D view of
lung tumor and classies the nature of tumor by identifying the location of tumor attached
to the wall or parenchyma. Furthermore, diagnostic tool reduces the false positive rate by
improving the signicant accuracy. Moreover, this application will help in the reduction of
the overall cost and create a better environment for the screening of lung tumor using CT
scan (Medical Imaging), lessen the duration of diagnosis, and prognosis of the disease using
learning algorithm.
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