Intelligent Automation & Soft Computing
DOI:10.32604/iasc.2022.018974
Tech Science Press
Article
Multi-Model Detection of Lung Cancer Using Unsupervised Diffusion
Classification Algorithm
N. Jayanthi1,*, D. Manohari2, Mohamed Yacin Sikkandar3, Mohamed Abdelkader Aboamer3,
Mohamed Ibrahim Waly3 and C. Bharatiraja4
1
Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, Tamilnadu,
India
2
Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, Chennai, 600119, Tamilnadu, India
3
Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952,
Saudi Arabia
4
Department of EEE, SRM Institute of Science and Technology, Chennai, 603203, India
*Corresponding Author: N. Jayanthi. Email: jayanthi.nkpr@gmail.com
Received: 28 March 2021; Accepted: 25 June 2021
Abstract: Lung cancer is a curable disease if detected early, and its mortality rate
decreases with forwarding treatment measures. At first, an easy and accurate way
to detect is by using image processing techniques on the cancer-affected images
captured from the patients. This paper proposes a novel lung cancer detection
method. Firstly, an adaptive median filter algorithm (AMF) is applied to preprocess those images for improving the quality of the affected area. Then, a supervised image edge detection algorithm (SIED) is presented to segment those
images. Then, feature extraction is employed to extract the mean, standard deviation, energy, contrast, etc., of the cancer-affected area. Finally, an unsupervised
diffusion classification (UDC) algorithm is explored to narrow down the affected
areas. The proposed lung cancer detection method is implemented on two datasets
obtained from standard hospital real-time values. The experiment results achieved
superior performance in the detection of lung cancer, which demonstrates that our
new model can contribute to the early detection of lung cancer.
Keywords: Diagnose lung cancer; future extraction; preprocessing; segmentation;
UDC algorithm
1 Introduction
Lung cancer is one of the most dangerous diseases afflicting people. It can typically be diagnosed using
image processing techniques to quickly identify affected cancer areas, thereby decreasing decrease its
development in time. Several factors may influence a rapid diagnosis of lung cancer, including tumor
growth, late mortality due to uncertain efficacy, lack of specific screening, and rapid disease progression
symptoms. The disease diagnosis depends, as well, on its performance dates. In the past few decades, the
accuracy of these values has been declining, and consequently slowing down the pace of the fight against
This work is licensed under a Creative Commons Attribution 4.0 International License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
1318
IASC, 2022, vol.31, no.2
lung cancer. For example, until recently, most lung cancers are being treated more or less as the same disease,
without any distinction to their variability.
Fig. 1 shows the example of the lung cancer image, image processing is used to analyze cancer affected
in this proposed system. The respiratory system is used for breathing, consisting of tissues, a network of
organs, blood vessels, and airways. It has been considered that common cancers are familiar and resist
controlling the disease. Meanwhile, they give rise to modification, which may require different treatment
methods in the medical field. A number of patients are used to check the disease’s level and overcome
the problem to reduce and achieve a better performance value. Hence, the proposed model ends up with
various tests that easily process the image to identify affected areas.
Figure 1: A basic lung cancer image
The methods presented here focus on improving the image quality to enhance the appearance of early
signs of cancer. Since the time coefficient imaging in the clinical field is generally utilized in the beginning
phases, it is significant to the therapy of cellular breakdown in the lungs by recognizing malignancy cells.
The lung image enriches the information of nature image data, and its nature is motivated by
preprocessing. Normally, there are mainly two image enhancement techniques: one is the spatial-domain
processing method, and the other is the frequency-domain processing method. The former is to directly
process the pixels of the image, which is basically based on the grayscale mapping transformation. The
latter is to perform operations on the transformed coefficients in a certain transform domain of the image
and then transform it to the original spatial domain to obtain an enhanced image. In this paper, an
unsupervised diffusion classification (UDC) technology is proposed, which is very simple to understand
and useful for real-time implementation. Using UDC in image classification can improve image linearity
and increase accuracy.
The main contributions of this paper are as follows.
1. To easily identify affected cancer region based on the unsupervised diffusion classification (UDC)
algorithm;
2. The time consumption and loss are less to identify the affected region.
The rest part of this paper is organized as follows: Section 2 introduces related work. Section 3 describes
the proposed method in detail. Section 4 presents the experimental results and discussions. Finally, Section
5 draws the conclusion.
IASC, 2022, vol.31, no.2
1319
2 Literature Review
Cancer is an essential disease that causes increased mortality levels worldwide. Neurotic determination
and the arranging of tumor tissue tests are giving the promise to treat illness, but it does not give accurate
affected regions [1]. Lung cancer is caused by the uncontrolled growth of aberrant cells in the human
body and healthy lung tissue development [2]. Early detection of lung cancer is important for patient
survival and quality of life [3]. Besides, detailed anatomical information is of absolute importance before
the actual process of pulmonary cavity amputation surgery planning. From Fig. 1, we can see that a
normal human lung has five different anatomical partitions, called lobes. There is a crack at the border of
each lung. Lungs typically include left and right parts, with upper, middle, and lower lobes and isolation
[4]. The lung segmentation depends on estimated precision and processing time, and the most popular
lung segmentation method is utilized in several image processing methods [5].
Image processing of lung cancer in the clinical field is broadly utilized in the beginning phases. Patients’
lung detection needs to recognize disease cells due to the significant time of their therapies and the impact of
cellular breakdowns in the lungs [6]. To achieve accurate detection performance, lungs must be extracted
based on anatomical perspectives. It should be noted that all external and internal pulmonary vessels are
removed in addition to the lobe bronchi. Further, motion assist of lung morphology is extracted from
binary images [7]. The valve function of quantitative evaluation automatically develops image registration
between lung and image segmentation by combining a series of lobe imaging tools [8].
The method based on local invariant features shows excellent results of this task. Local features’ values
directly differ from their properties and the natural pattern of the associated images [9]. Images of irregular
breathing and heartbeats are twisted and presented at different noisy frequencies [10]. Therefore, the purpose
of preprocessing is to enhance images and provide an automatic image processing technique that includes a
human observer’s image to enter the starting phase of image information [11]. In preprocessing, the input CT
images are processed by improving the image quality. In this stage, several actions are performed on the
model data is used to highlighted images [12]. Feature extraction is an important step to determine
normal or abnormal images, which is the basis of the classification process [13]. The extracted tumor
features are the average length of perimeter, area, and eccentricity, which can be completely determined if
there is a binarization of a cancer tumor. Meanwhile, if there is a malignancy, the cancer stage can also
be identified [14].
Image enhancement based on Gabor function is simultaneous (and optimal) to local log with excellent
spatial and frequency domains and local multi-scale decomposition [15]. As mentioned above, a radiologist
can become a decision-maker to identify abnormalities from original CT images. The center and diameter of
clinical cancer data are used as the initial segmentation seed for our computer-aided design (CAD) program
[16]. The concepts of segmentation and detection are, however, not equivalent. The major and minor details
data can be incomplete, while at the same time, other accessory cracks can be visible [17]. Therefore,
extracting basic features of nodules requires many heuristic steps [18]. The calculation is utilized to
improve the lung parenchyma’s applied slope image to distinguish the four associated networks around
every pixel of the object with the least angle estimation [19]. Also, the alignment of gradient direction
calculation on high contrast edges is applied to determine the standard image plane and 3D image
gradient vector [20]. Elastic registration using full scan and cracking is refined by mutual information
registration programs such as a similarity measure [21].
3 Materials and Methods
Globally, lung cancer is one of the most serious diseases because its identification is complicated in the
medical field. Lung cancer scan is currently not advocated, especially for the high-risk population. The most
significant use is identifying central tumors and cancers. In this paper, imaging techniques are employed to
1320
IASC, 2022, vol.31, no.2
predict the early detection of lung disease disorders. Firstly, the Adaptive Median Filter algorithm is applied
to preprocess the input images. Then, the Supervised Image Edge Detection is exploited to distinguishing
unconventional image variety. Finally, the unsupervised diffusion classification (UDC) is proposed to
classify this system’s images. In this model, the feature detection and accuracy analysis are taken for the
lung cancer image-based UDC technique.
Preprocessing
Adaptive Median
Filter
Segmentation
Supervised Image
Edge Detection
Input Image
Classified results
UDCA
Feature extraction
Output Image
Figure 2: The framework of lung cancer detection
Fig. 2 illustrates the framework of the proposed strategy, which consists of seven stages: input,
preprocessing, segmentation, feature extraction, UDC, classification, and output.
3.1 Preprocessing
Image preprocessing is a significant start in the diagnosis of lung symptoms, which is conducted to
improve the overall performance of lung cancer classification. In our preprocessing, the adaptive median
filter is mainly utilized to reduce the random noise of the input image with an excellent noise reduction
function. After preprocessing, the image quality is improved compared to the original image.
The median filter method is a nonlinear smoothing technique, which sets the gray value of each pixel to
the median of the gray values of all pixels in a certain neighborhood window of that point. Adaptive median
filters have been widely used in image enhancement, feature extraction, and pattern medical multistage
expression.
Adaptive Median Filter
Step 1: Select the image size and process the value of the pixel pðx; yÞ in the optimized input image data.
Step 2: Analyze the ascending order of the pixel value in the image and denote the pixel value by median
value Pmed .
Step 3: Sort the minimum pixel value Pmin and the maximum pixel value Pmax by vector V0. The sorted
vector V0 is the combination of Pmax and Pmin of the system, where Pmax is maximum pixel value, Pmin is
minimum pixel value, and V0 is vector value.
IASC, 2022, vol.31, no.2
1321
Step 4: If the range of Pmax and Pmin is within the image pixels analysis, then pðx; yÞ is an uncorrupted
pixel or a corrupted pixel.
Step 5: If the condition of Step 4 is not met, the neighboring pixel crosses to result in the difference
vector VD, which can find the largest difference in the image V0 corresponding to the processed pixel to
enhance the output image.
where,
Pmin = minimum value of the pixel
Pmax = maximum value of the pixel
Pmed = median pixel value
V0 = sorted vector
VD = difference vector
Step 6: End.
We can see from the above Fig. 3, Fig. 3a shows the input image, and Fig. 3b displays the preprocessed
image using the Adaptive Median Filter. The preprocessed image is enhanced the image quality and is fed to
the next stage—the segmentation process.
Figure 3: The input and output of the image preprocessing stage. (a) The input image (b) The preprocessed
output image
3.2 Segmentation
Image segmentation is an essential process of extracting meaningful features and areas from an image,
which is a hotspot in the image field. These features can be the original characteristics of the image, such as
gray pixel values, colors, reflection characteristics, and textures. The algorithm’s main idea is to cluster
pixels, i.e., the image is divided into many blocks depending on the relationships between adjacent
pixels. In this stage, the correlation between pixels can be explained by the distance.
Supervised Image Edge Detection Algorithm
Step 1: Obtain the preprocessed image.
Step 2: The threshold value T is utilized.
If (p, q) > T, g (p, q) = 0;
If (p, q) ≤ T, threshold value.
1322
IASC, 2022, vol.31, no.2
where p; q are input variables.
Step 3: If the threshold T value is measured with the input variable. Depending on the local threshold or
neighborhood, if T (p; q) area. The versatile thresholding, if T is an element of (p; q).
Step 4: The various process of the filter compared to the threshold value
If g (p; q) > T2, g (p; q) = b;
If T1 < f (p; q) ≤ T2,
If g (p; q) ≤ T1.
where,
T = Threshold value
p; q = input variable
Step 5: End.
Fig. 4 illustrates the input and output images of the segmentation stage. Fig. 4b denotes the affected area.
Figure 4: The input and output images of the segmentation stage. (a) The image before segmentation (b)
The image after segmentation
3.3 Feature Extraction
An invariant feature of the image is extracted, depending on the selected condition and the constant
function’s value. During image feature extraction, it finds the reliable and robust shape, appearance, and
values of any parameters that can control the contrast, as long as the shape image is in the presence of
light and dark. In feature extraction, the adaptive median filter can effectively affect the liver and
distinguish it from normal organs. Thus, Pmin and Pmax can be defined as:
P
Ixþi;yþj Tx;y
P min ¼ 1 2
ðx;yÞ2w
P
ðx;yÞ2w
I2xþi;yþj
(1)
IASC, 2022, vol.31, no.2
P
P max ¼
ðx;yÞ2w
1323
Ixþi;yþj Tx;y
P
ðx;yÞ2w
I2xþi;yþj
(2)
where the constant variable is the first term and the optimal value of the image. And Ii;j is pixel mean value,
i; j is pixel probability value, Ixþi;yþi is image points, and T is pixels template.
P
Ixþi;yþj Tx;y
max ¼
ðx;yÞ2w
P
ðx;yÞ2w
I2xþi;yþj
(3)
In general, it is useful for each gray level in the normalized template image. Given the pixel (x, y) at the
position corresponding to the normal distribution, then the max can be expressed as:
P
ðIxþi;yþj Ii;j ÞðTx;y TÞ
max ¼
ðx;yÞ2w
P
ðx;yÞ2w
ðIxþi;yþj Ii;j Þ2
(4)
where,
Ii;j = pixel mean value
i; j = pixel probability value
Ixþi;yþi = image points
T = pixels template
This model has been considered at several levels and presents different image details at different scales.
However, as a constant, the adaptive median filter can adequately and directly account for uncomplicated
lung problems.
3.3.1 Mean
The mean filter operation is mainly based on arithmetic averaging and variation of Gaussian noise from
video signals.
M X
N
1 X
Pij
f ðx; yÞ ¼
MN i¼1 j¼1
(5)
where M and N are the image dimension and the total number of pixels, respectively. Pij is the color value at
the ith column and the jth row.
3.3.2 Standard Deviation
Standard deviation is the most generally utilized method to calculate the changeability or decent variety.
As far as image preprocessing, it indicates the amount of variation from the average (mean or expected
value). Thus, the standard deviation SD can be defined as:
1324
IASC, 2022, vol.31, no.2
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
jp qj2
SD ¼
M
(6)
where Σ is the sum function, pis the input image, q is the image mean value, and M is the number of data
points from the input image.
3.3.3 Energy
Energy is used to measure the smoothness of the image. The energy of the pixel extends between 0 to 1.
Consistency is transferred pixels, and the energy can be expressed as:
vffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u q1
uX
(7)
p2i;j
Energy ¼ t
i;j¼0
where Pi;j is the pixel color value of the ith column and the jth row.
The less smooth the area, the more consistent the dispersion. Pi;j and the lower the rakish second to be
estimated. Pi;j is the sixth section of the standardized co-event lattice.
3.3.4 Contrast
Black and white contrast and the polarization distribution are used to capture the image’s grayscale
dynamic range. Therefore, The contrast measure is defined as:
Contrast ¼
N1
X
Pi;j ði jÞ2
(8)
i;j¼0
where Pij denotes the color value of the ith column and the jth row, and N is the number of images.
3.4 Unsupervised Diffusion Classification (UDC)
The proposed unsupervised diffusion classification (UDC) is a more straightforward learning calculation
for separating arrangement maps into various element vector planes by nonlinear planning, normally various
classes by high-dimensional planes. It works out two sorts of characterization of the hyperplane.
Unsupervised Diffusion Classification Algorithm Steps
Step 1: Initialize input data.
Step 2: Define the value of pixel P(x, y), partial area B, and all the input data pixels.
Step 3: Calculate the maximum pixel vector near each erosion and dilation B’s expanded
configuration.
Pðx; yÞ ¼ argminði;jÞ2z2 ðBÞ fPB ½M ðx þ i; y þ jÞg
Pðx; yÞ ¼ argmaxði;jÞ2z2 ðBÞ fPB ½M ðx þ i; y þ jÞg
where P is the pixel value.
Step 4: Update the image at each location (x; y) using
ðP; QÞ ¼ Dist½Pðx; yÞ
IASC, 2022, vol.31, no.2
1325
Step 5: If the pixel is set to the maximum, then loop Step 5 and let I + 1; Otherwise, using F ¼ f B,
the original image is extended and replaced.
Step 6: A unique spectral set of pixels with a higher correlation in the form vectors is used to calculate
the pixel vector pair of pixels P.
Step 7: End.
Unsupervised diffusion classification (UDC) is originally designed for binary classification with the sum
of N (N − 1)/2 categories. Carriers may be divided into a maximum number of values of each particular type.
4 Results and Discussion
The detection of the affected cancer region in the lungs with the help of the image processing technique
is based on the proposed UDC technique. The proposed unsupervised diffusion classification (UDC)
performs superior by accomplishing exactness, affectability, and explicitness when contrasted with
another ordinary classifier. Our experiment is conducted on MATLAB 2017a, which is one of the most
widely used programs. Particular activities and imaginative conditions to execute the calculation are
broken down, as shown in Fig. 5. Fig. 5 shows the process of image preprocessing, segmentation, feature
extraction, and accuracy. This GUI shows 90% of accuracy by using the proposed UDC technique.
Figure 5: The screenshot of the proposed system
Tab. 1 shows the configuration details used in our experiment, including datasets, persons in analysis,
lung cancer images, and the software.
1326
IASC, 2022, vol.31, no.2
Table 1: Configuration details
Configuration
Details
Datasets
Person in analysis
Lung cancer images
Software
TCIA (The cancer imaging archive) and LCTD (Lung CT Diagnosis)
20 persons
300 images
MATLAB image processing toolbox
Tab. 2 illustrates the abnormal image feature values analysis of the LCTD dataset.
Table 2: The feature value of abnormal images based on LCTD dataset
Image Sample
Mean (DB)
Standard deviation (σ)
Contrast
Energy (J)
1
2
3
4
0.00343698
0.00280874
0.00279415
0.00287841
0.0897489
0.0897708
0.0897712
0.0897686
0.248331
0.286713
0.314562
0.224536
0.739322
0.764703
0.765458
0.753461
Fig. 6 displays the different image features via the proposed unsupervised diffusion classification (UDC)
method.
Figure 6: Image feature value comparison
Tab. 3 shows the comparison of the different methods UDC, back propagation network (BPN), and
principal component analysis (PCA), and the proposed unsupervised diffusion classification (UDC)
method achieves an accuracy of 90%.
IASC, 2022, vol.31, no.2
1327
Table 3: Comparison of UDC, BPN, and PCA based on TCIA and LCTD datasets
Measures
UDC
BPN
PCA
UDC dataset 1
(TCIA) (%)
UDC dataset 2
(LCTD) (%)
BPN dataset 1
(TCIA) (%)
BPN dataset 2
(LCTD) (%)
PCA dataset 1
(TCIA) (%)
PCA dataset 2
(LCTD) (%)
Accuracy
89
90
74
76
73
74
Precision
86
88
72
74
70
72
Sensitivity 85
87
71
72
69
70
Specificity 83
85
69
70
67
69
Fig. 7 illustrates the performance of four evaluation metrics among UDC, BPN, and PCA on TCIA and
LCTD datasets.
100%
90%
80%
Values (%)
70%
UDCA Dataset 1 (TCIA)
60%
UDCA Dataset 2 (LCTD)
50%
BPN Dataset 1 (TCIA)
BPN Dataset 2 (LCTD)
40%
PCA Dataset 1 (TCIA)
30%
PCA Dataset 2 (LCTD)
20%
10%
0%
Accuracy
Precision
Sensitivity
Specificity
Figure 7: Values of four metrics’ performance
Fig. 8 and Tab. 4 show the false ratio comparison of the existing method and the proposed method on
two datasets. It is clear that the unsupervised diffusion classification (UDC) method has the best performance
with a very less false ratio.
Figure 8: The false ratio of UDC, BPN, and PAC on TCIA and LCTD datasets
1328
IASC, 2022, vol.31, no.2
Table 4: The false ratio of UDC, BPN, and PAC on TCIA and LCTD datasets
Measures
UDC dataset 1
(TCIA)
False ratio (%) 11
UDC dataset 2
(LCTD)
BPN dataset 1
(TCIA)
BPN dataset 2
(LCTD)
PCA dataset 1
(TCIA)
PCA dataset 2
(LCTD)
10
26
24
27
26
5 Conclusion
Lung disease of the respiratory system may hide details. Generally, clinicians rely on automatic
classification schemes rather than estimating direct observations of respiratory parameters, which is
missing from previous studies and is difficult to understand by providing a mechanism to explain
menstruation. In this paper, the proposed model shows the patient’s health conditions with the control of
lung cancer. This model considers the performance parameters, which can be of great importance in the
practical diagnosis of the input image. The exploratory outcomes demonstrate that the parameters, such as
mean, standard deviation, contrast, energy, are promising for identifying lung cancer. In this model, the
comparative analysis confirms the proposed unsupervised diffusion classification (UDC) is superior to
existing methods, with the accuracy of 90%, the sensitivity of 87%, the precision of 88%, and specificity
of 85%, respectively.
Acknowledgement: We would like to thank TopEdit (www.topeditsci.com) for the English language editing
of this manuscript.
Funding Statement: The authors received no specific funding for this study.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the
present study.
References
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
T. Jayasankar, N. B. Prakash and G. R. Hemalakshmi, “Big data-based breast cancer prediction using kernel
support vector machine with the gray wolf optimization algorithm,” in Applications of Big Data in Healthcare,
Academic Press, pp. 173–194, 2021.
M. Anuradha, T. Jayasankar and N. B. Prakash, “IoT enabled cancer prediction system to enhance the
authentication and security using cloud computing,” Microprocessor and Microsystems, vol. 80, pp. 1–14, 2021.
R. Sammouda, “Sammouda, segmentation and analysis of CT chest images for early lung cancer detection,” in
Proc. GSCIT, Sousse, Tunisia, pp. 5090–2659, 2017.
Q. Wei, Y. Hu, G. Gelf and J. H. MacGregor, “Segmentation of lung lobes in high-resolution isotropic CT
images,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 5, pp. 1383–1393, 2009.
E. Hosseini, M. Jacek and K. Zurada, “3D lung segmentation based on incremental constrained nonnegative
matrix factorization,” IEEE Transactions Biomedical Engineering, vol. 65, no. 5, pp. 18–9294, 2015.
S. Avinash and S. Senthilkumar, “Analysis and comparison of image enhancement techniques for the prediction of
lung cancer,” in Proc. RTEICT, Bangalore, India, pp. 5090–3704, 2017.
K. Punithavathy and M. M. Ramya, “Analysis of statistical texture features for automatic lung cancer detection in
PET/CT images,” in Proc. RACE, Chennai, India, pp. 81–94, 2015.
H. Haneishi, H. Ue, N. Takita, H. Toyama, T. Miyamoto et al., “Lung image segmentation and registration for
quantitative image analysis,” in Proc. IEEE NSSCR, San Diego, USA, vol. 3, pp. 1390–1393, 2002.
H. Madzin and R. Zainuddin, “Feature extraction and image matching of 3d lung cancer cell image,” in Proc.
ICSCPR, Malacca, Malaysia, pp. 7695–3879, 2009.
IASC, 2022, vol.31, no.2
1329
[10] J. Wei and G. Li, “Automated lung segmentation and image quality assessment for clinical 3-D/4-D-computed
tomography,” IEEE Journal of Translational Engineering in Health Medicine, vol. 2, no. 6, pp. 2168–2372, 2015.
[11] M. S. Al-Tarawneh, “Lung cancer detection using image processing techniques,” Leonardo Electronic Journal of
Practices and Technologies, vol. 11, no. 21, pp. 147–158, 2012.
[12] A. S. Oliver, M. Anuratha, M. J. Justus, K. Bellam and T. Jayasankar, “An efficient coding network-based feature
extraction with support vector machine-based classification model for CT lung images,” Journal of Medical
Imaging and Health Informatics, vol. 10, no. 11, pp. 2628–2633, 2020.
[13] A. Vijay and K. Gajdhane, “Detection of lung cancer stages on CT scan images by using various image processing
techniques,” Journal of Computer Engineering, vol. 16, pp. 28–35, 2014.
[14] S. S. Kanitkar, N. D. Thombare and S. S. Lokhande, “Detection of lung cancer using marker-controlled watershed
transform,” in Proc. ICPC, Pune, India, pp. 1–6, 2015.
[15] H. Gujral and K. Deulkar, “A review of techniques for lung cancer detection,” International Journal of Current
Engineering and Technology, vol. 5, pp. 2347–5161, 2015.
[16] N. Emaminejad, W. Qian, Y. Guan, M. Tan, Y. Qiu et al., “Fusion of quantitative image and genomic biomarkers
to improve prognosis assessment of early-stage lung cancer patients,” IEEE Transactions on Biomedical
Engineering, vol. 63, no. 5, pp. 1034–1043, 2015.
[17] I. Sluimer and A. Schilham, “Computer analysis of computed tomography scan of the lung: A survey,” IEEE
Transactions on Medical Imaging, vol. 25, pp. 385–405, 2006.
[18] A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs et al., “Pulmonary nodule detection in CT images: False
positive reduction using multi-view convolutional networks,” IEEE Transactions on Medical Imaging, vol. 35,
no. 5, pp. 1160–1169, 2016.
[19] J. Song, C. Yang, C. Yang, L. Fan, K. Wang et al., “Lung lesion extraction using a toboggan based growing
automatic segmentation approach,” IEEE Transactions on Medical Imaging, vol. 35, no. 1, pp. 337– 353, 2015.
[20] F. Beaulieu and D. Rubin, “Surface normal of the overlap a computer-aided detection algorithm with the
application of lung nodules in helical CT,” IEEE Transactions on Medical Imaging, vol. 23, pp. 661–675, 2004.
[21] I. Sluimer and M. Prokop, “Toward automated segmentation of the pathological lung in CT,” IEEE Transactions
on Medical Imaging, vol. 24, pp. 1025–1038, 2005.