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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 identication and 3D reconstruction of lung using CT-Scan. 3C TecnologÃa. 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 dierentiate between the types of tumor through automated analysis and
increase in accuracy. The system is aggregated model for tumor identication 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 signicant
accuracy. The proposed diagnostic software provides signicant results with bright CT scans
to identify lungs tissue with dierent orientations by rotating it and reduces the enormous
false positive rate by increasing the eî™»ciency 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 benets 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 decient 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 articial neural network
(ANN) accompanied by training the framework for classication 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 identication 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 dierentiate 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 eective use of predictive computer-aided design
(CAD) for lung tumor diagnosis (Tiwari, 2016; Ritika et al., 2011). An enhanced SVM
classier was used for diagnosing leukemia tumor using a fast-correlation-based lter to
choose the most inuential 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 dierent 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 identied 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 identication 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 predened 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 dierent
classiers 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 articial 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
classication (Dev et al., 2019).
MATLAB software has been applied to implement all the procedures in the proposed
system. Dierent stages entailed image pre-processing, segmentation, SVM classication,
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). Dierent 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 eective 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 dierent
orientations and other functionalities for detecting lungs tumor including feature extraction
nodule detection by learning classier, active contour-based nodule contour extraction, and
nodule connectivity recognition by tissue classication. 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 classies 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 dierent orientations by rotating it and reducing
the enormous false positive rate by increasing the eî™»ciency 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 signicance 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 dierent clinical contexts such as mammography screening and nodule
detection. Secondly, learning classier can enhance classication performance by stimulating
the less experienced or non-experienced clinicians in order to evaluate the risk of a specic
nodule being malignant.
2. DESIGN PHASES
This paper has divided its objective in three dierent phases.
Firstly, this study extracted or selected dierent features for the 3D construction. Secondly,
this study has developed learning classier. Lastly, the study has designed a tool for integrated
solution. In the rst phase, personnel from two dierent 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 signicant 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 classier based on recognition percentage value. These results
were used to develop the combination of dierent classiers with derived features to select
the most accurate classiers in particular situation. The result of the classier identied
lung tumor with appropriate features and learning classiers.
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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 dierent 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 signicant features, classiers, 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 classication, symptoms and nodule classication
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 dierent 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, diî™»culty 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 classications 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 classication of tumor area, perimeter, diameter,
centroid, robustness, smoothness, indentation, and calcication along with pixel value for
tumor classication.
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Figure 1. Benign Pulmonary Nodule with smooth edge and central calciî‚¿cation.
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 dierent set of tools used
to develop the software for 3D reconstruction and identication of lungs tumor. MALAB
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tool was used for Graphical User Interface (GUI), image segmentation for getting 3D
functionalities, and identication 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 oers 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 dening decision boundaries, as the idea related about decision
planes. Simple technique for nding decision planes that a set of objects separates out
between dierent 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 classication that dierentiates
the two classes very well.