A REVIEW ON COMPUTERIZED PULMONARY NODULE DETECTION …
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A REVIEW ON COMPUTERIZED PULMONARY NODULE DETECTION IN CT
IMAGES
1P. Malin Bruntha,
2S. Immanuel Alex Pandian
1Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences,
Coimbatore, Tamilnadu, India
2Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences,
Coimbatore, Tamilnadu, India
Abstract
Cancer is one of the deadliest diseases in the world. Lung cancer, a type of cancer is
the leading cause of deaths among cancer deaths. Early detection and diagnosis of the disease
is essential to prolong the life of the patients affected with this scourge. To facilitate this,
Computer Aided Detection and Diagnosis systems have emerged. Many authors have applied
various techniques to successfully detect and diagnose the pulmonary nodules. A systematic
review of these techniques is the need of the hour. This paper aims to address these critical
concerns.
Key Words: Lung Pulmonary Nodules, Preprocessing, Segmentation, Nodule Detection,
Classification.
1. Introduction
Cancer is a leading cause of death among diseases in human beings [1]. According to
International Agency for Research in Cancer, World Health Organization, in the year 2012
alone, 14.1 million newer cancer cases were detected [2]. Among the various types of cancer,
lung cancer is the leading cause of cancer deaths worldwide. The occurrence of lung cancer
worldwide is 13% (out of 14.1 million cases). It is about 15.5% (out of 6763030) in Asia and
6.9% (out of 1014934) in India [3].
The people who are diagnosed with lung cancer have a 5 year survival rate in between
10-16% if the disease is in advanced stages. But if it is detected at early stages, the 5 year
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survival rate increases dramatically to 70% [4]. This underscores the need for early detection
of lung cancer by the latest technological tools at our disposal.
A pulmonary lung nodule is defined as having focal opacity of 3 to 30 mm diameter
nodules. If the sizes are less than 3 mm, then they are termed as micro-nodules. If the sizes
are greater than 30 mm, then they are termed as “mass” [5]. It can be classified further with
respect to their position and location [6]. These lung nodules may not have any attachment
with neighbouring structures with well circumscribed nodules. Juxta-pleural nodules are
attached to lung parenchyma and juxta-vascular nodules are attached to vessels. These
nodules can be solid, sub-solid and in some cases, non-solid [7].
It is imperative to calculate the nodule size carefully since it is essential to determine
the malignancy factor. Inaccuracies may creep in if the nodules are non-spherical and if they
are measured manually [8,9]. Furthermore, the efforts taken to improve the detection of
cancer nodules from the available images by manual methods may have the following
difficulties like limitations of human visual systems, insufficient training given to the
radiologists who handle the images and fatigue of medical personals [10]. The workload of a
given radiologist may also indirectly affect his/her efficiency [11]. These may lead to
misinterpretation of data or may result in error in judgment. Hence, it is essential to have
automated methods to detect, measure and diagnose these pulmonary modules.
Computerized Tomography (CT) which became popular and more available in 1970s
has become one of the most important modality for imaging small lung modules. The people
with high risk of getting lung cancer such as smokers have been screened with low-dose CT
(LDCT) scans in order to facilitate early detections of lung cancer [12]. A major challenge is
to detect, segment and classify the nodules that will assist the radiologists in pinpointing the
possible existence of abnormalities. Such systems are called as Computer Aided Diagnosis
(CAD) systems. They provide specific information about these nodules [13].
One can classify CAD systems into two types. The first one is Computer Aided
Detection system (CADe) and the second one is named as Computer Aided Diagnostic
system (CADx) [14]. Through CADe system, the radiologists can identify Regions of Interest
(RoI) in the given image that reveals malignancy. By using CADx system, one can come to
know about the identification of the disease, its type and its severity. By using CADx, it is
possible to tell the stage of cancer and its progression or regression. CT scans can be used to
diagnose the level of malignancy. This would avoid unnecessary repeated CT scans.
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In short, CADe systems can efficiently reduce the workload of radiologists and also
reduce the time taken for analyzing a particular image thereby enabling them to screen more
patients within a given span of time. They can assist efficiently in early detection of lung
cancer. They can improve the pulmonary nodule detection accuracy [7].
There have been many systematic reviews in the past done by many authors on this
subject in order to analyze and appreciate the performance of the best techniques available
and developed up to that point of time. For example, Lee et al., has carried out a systematic
review on automated detection of lung nodules from CT images [6]. Suzuki et al., has
undertaken a profound review of CADe in thoracic and colonic imaging [15]. Eadie et al., has
critically reviewed the usage of CADx in diagnostic cancer imaging [16]. Few other reviews
were carried out by Firmino et al [7], El-Baz et al [17] and Igor Rafael S. Valente et al [13].
But, it is necessary to conduct a critical review time and again in order to include the latest
techniques developed very recently. It is with this aim, this review of literature has been
carried out. For the review of literature, a total of 70 works from Web of Science, PubMed,
IEEE Xplore, Science Direct and others have been used.
2. Data Acquisition
The preliminary step in any image processing is getting images from the source. In
this review, the papers which used CT imaging modality are collected. Few papers used the
private datasets of lung CT images, obtained from hospitals and from national screening
programs. The available public databases are very helpful to the researchers to validate their
algorithm. The popular lung CT databases are LIDC-IDRI, NELSON, ELCAP, SPIE-AAPM,
Kaggle’s Data Science BOWL 2017 and ITALUNG-CT. Figure 1 represents a typical lung
CT image.
Figure 1. Lung CT image
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3. Pre-processing
Pre-processing is a preliminary process in image processing to enhance the quality of
the input image. This process eliminates the extraneous details present in the lung CT image.
This step ensures the given image is free from noise and any artefacts. The following pre-
processing methods are mostly used in the reported papers. They are: Gray level
thresholding [18,32,40],Gaussian smoothing [40,54,69] andTrilinear Interpolation
[26,35,37,41].
Other techniques used in many other papers are Morphology Operation[20], Streak
Detection Filter[21], 2D enhancement filters[24], Isotropic resampling [25], Cylinder Filter,
Spherical Filter [29,34], Convolution with a Gaussian kernel [31], 2D flooding [40], selective
filters [41],Angiometric diffusion model [42], Down sampling, Contrast enhancement [44],
Anisotropic diffusive filter [49,60,63], Median filter [53,54,64], 3D Coherence Enhancing
Filter [54], Contrast Stretching [68], Histogram Equalization [69], Discrete wavelet transform,
Unsharp energy masking [71], Type II fuzzy algorithm [76] and Bicubic interpolation [77].
The preprocessing techniques are listed in Table 1 according to the chronological order.
Table 1. Literature review of preprocessing
Authors Techniques used
Samuel G. Armato et
al [18]
Grey level profile
Tomakazu Oda et al
[20]
Morphology operation
Mitsuru Kubo et al
[21]
Streak Detection filter
Qiang Li et al [24] 2D enhancement filters
William J. Kostis et
al [25]
Isotropic Resampling
David S. Paik et al Trilinear interpolation
Sukmoon Chang et
al [29]
Cylinder filter, spherical filter
Paulo R. S.
Mendonca et al [31]
Convolution with a Gaussian kernel
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TemesguenMessay
et al [44]
Down sampling, contrast enhancement
Stefano Diciotti et al
[49]
Anisotropic diffusive filter
Ezhil E. Nithila et al
[69]
Histogram equalization, Gaussian filtering,
Farzad V Farahani et
al [76]
Type II fuzzy algorithm
4. Lung Segmentation
Segmentation is the process of separating the lung parenchyma from the lung CT
image. Before detecting the required candidate nodules, in most of the papers, the lung
region was extracted based on thresholding, shape and border. The popularly used
segmentation techniques were summarized from the selected papers. They are Gray level
histogram [18], Simple thresholding [19,21,24,26,44,49,51,61,63,64], Labelling
[21,43],Gray-level thresholding [22,32,68], Dilation [26,32,43], 3D region growing algorithm
[31,77], Adaptive thresholding [43,47],2D region growing algorithm with rolling ball
algorithm [46,52,60],3D connected component analysis [59,61,63], Fuzzy c-means algorithm
[60,71].
The other segmentation techniques used in the papers are Negative Masking
[26],Linear discriminant analysis [27], Region growing algorithm with active contour model
[36], Genetic cellular neural network [39], Inner border tracing algorithm and adaptive border
marching algorithm [40], Fuzzy thresholding [42], Erosion[43], Coupled competition and
diffusion process [48], Robust active shape model matching algorithm [50], Greedy snake
algorithm [51], Statistical intensity based approach [55], Otsu thresholding [56,70,77],
morphological opening [56], Active contour model with level set method [58], Optimal
thresholding, , Adaptive curvature thresholding [60], high level vector quantization [61],
Morphological closing[61,63], Active contour model with signed pressure function [69],
entropy algorithm [71], Modified spatial kernelized fuzzy c-means clustering [76]. In Table 2,
the segmentation algorithms are listed as per the chronological order.
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Table 2. Literature review of segmentation
Authors Segmentation
Hidetaka Arimura et al [27] Linear discriminant analysis
Paulo R. S. Mendonca et al [31] 3D region growing algorithm
SerhatOzekes et al [39] Genetic cellular neural network
Jiantao Pu et al [40] Inner border tracing algorithm, adaptive border
marching algorithm
J.R.F. da Silva Sousa et al [46] 2D Region Growing Algorithm, Rolling ball
algorithm
Shanhui Sun et al [50] Robust Active Shape Model Matching Algorithm
Elizabeth et al [51] Thresholding and greedy snake algorithm
Jinsa Kuruvilla et al [56] Otsu thresholding, morphological opening
TemesguenMessay et al [62] 3D global segmentation, multiple successive 2D
rolling ball filters
Ezhil E. Nithila et al [69] New active contour model with new signed
pressure function
Farzad V Farahani et al [76] Modified Spatial Kernelized Fuzzy C-Means
clustering
Jing Gong et al [77] Otsu thresholding, 3D region growing,
morphological operations
5. Candidate Nodule Detection
After segmenting the lung region, the region of interest (ROI) will be identified and
segmented from the other parts of the lungs. This part is very crucial because some nodules
are attached with pleural walls while some nodules are attached with blood vessels. Any dot
or blob like structures are segmented using the following prevalent techniques viz.,
Multiplegray level thresholding [18,25,27,35,44], 2D connected component labelling method
[24,25], Thresholding [29,34,41,44],Region growing algorithm along with connected
component analysis and morphological operation [32,36] and 3D Region growing algorithm
[37,41,48].
Other techniques for nodule detection are Genetic algorithm template matching
[19,28], 3D labelling method [20], Laplacian filters [21],Surface normal overlap [26],
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Matched filter, Ring filter [27], 3D morphological matching algorithm [32], Gradient
thresholding[35], Sphericity oriented contrast region growing and fuzzy connectivity map
[38], 8 directional search method with distance threshold [39], Adaptive thresholding [43],
3D labelling technique [43], Expert filtering [44], Skeleton based segmentation [46],
Marching Cube algorithm with radial basis function [47], Laplacian of Gaussian filters[49],
Erosion filter and pruning process [51], Growing neural gas [52], Optimal thresholding [54],
Expectation maximization algorithm with level set approach [55], Dot enhancement filter
[59],K-means clustering [68], Fuzzy c-means clustering [69], Spatial fuzzy c-means
algorithm [70], Gaussian smoothing [71], 3D region growing GrowCut algorithm [74], 3D
tensor filtering and local shape feature analysis [77]. Table 3 gives few of the important
nodule detection methods used by different authors.
Table 3. Literature review of Nodule detection
Authors Nodule Detection
Samuel G. Armato et al [18] Multiple Gray Level thresholding
Yongbhum Lee et al [19] Genetic Algorithm Template matching
Qiang Li et al [24] 2D connected component labelling
technique
William J. Kostis et al [25] Gray level thresholding, connected
component analysis, vascular subtraction,
pleural surface removal technique
Paulo R. S. Mendonca et al [31] Geometric and intensity model, eigen
values of curvature tensor
Kyontae T. Bae et al [32] 3D morphological matching algorithm
Xujiong Ye et al [42] Adaptive thresholding, modifier
expectation-maximization method
Jorge Juan et al [43] Adaptive thresholding, area test, 3D
labelling technique
Stelmo et al [52] Growing neural gas (GNG), 3D distance
transform
Amal A. Farag et al [55] Expectation Maximisation Algorithm,
variational level set approach
Muzzamil Javaid et al [68] K-means clustering, morphological
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opening
Jing Gong et al [77] 3D tensor filtering and local shape feature
analysis
6. Classification
Once the nodule candidates are extracted from the CT image, it is imperative to
classify whether it is normal nodule or abnormal nodule. This step is considered as the most
important step in CAD system because based on this, the radiologist can decide to treat and
follow up the patients. The features such as geometric, shape, texture is extracted from the
candidate nodules and these handcrafted feature vectors are given as input to the classifier.
For the deep learning convolution neural network, the feature extraction process is not
required [62,66,72,73].
The classification process is often considered as false positive reduction. The
following classifiers are popular in taking decisions between true nodule and false nodule.
They are: Linear Discriminant Analysis [18,22,27,57], Rule Based Classification [19-
22,27,31,32,41,42,43,58,61,68], Multiple Massive Training Neural Network [23,27,30], K-
nearest neighbour [34,74], SVM [42,46,52,54,59,61,65,67,68,77], Random Forest [45,70,77],
and Radial basis function neural network [51,74].
Other techniques adopted for reducing false positives are Area based
classification[24], Bayesian supervised classifier [28], double threshold cut and neural
network [36], 3D template using convolution based filtering and fuzzy rule based
thresholding [37],Fisher Linear Discriminant Classifier and Quadratic Classifier [44], Back
propagation neural network(BPNN) [56], Gentle Boost Classifier [57], Multi-layer
perceptron regression neural network [62], Multi-view convolutional neural network [66],
Particle swarm optimized BPNN [69], Multi-crop convolutional neural network [72], 3D
convolutional neural network [73], Naïve Bayes classifier [74], AdaBoostedBPNN [75],
Ensemble of multilayer perceptron, k-nearest neighbour and SVM [76], Logistic Regression
[77] and J48 Decision Tree [77]. The performance of the classifier/false positive reduction
methods are listed in the Table 4.
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Table 4. Literature review of classifiers
Authors Classifier Performance
Kenji Suzuki et al
[23]
Multiple Massive Training
Neural Network
Sensitivity 80.3%
No. of FP=0.18/case
Hidetaka Arimura et
al [27]
2 rule based scheme, multi-
MTANN, LDA
Sensitivity 83%
No. of FP=5.8/scan
Aly Farag et al [28] Bayesian supervised
classifier
Sensitivity 82.3%
No. of FP=9.2/scan
Jinghao Zhou et al
[34]
K-nearest neighbour Mean error rate 3.7%
No. Of FP = 1/scan
Bellotti et al [36] Double threshold cut and
neural network
Sensitivity 88.5%
No. of FP=6.6/scan
Jorge Juan et al [43] Linear Discriminated
analysis (LDA)
Sensitivity 80%
No. of FP=7.7/case
TemesguenMessay
et al [44]
Fisher linear discriminant
classifier, quadratic
classifier
Sensitivity 82.6%
No. of FP=3/scan
Lee et al [45] Random Forest Sensitivity 98.33%
Specificity 97.11%
Tong Jia et al [58] Rule based classification Sensitivity 90%
No. of FP=1/Scan
Hao Han et al. [61] Rule based filtering,
feature based SVM
Sensitivity 89.2%
No. of FP=4/scan
Arnaud A.A. Setio et
al [66]
Multi-view convolutional
neural networks
Sensitivity 85.4% –
90.1%
No. of FP=1-4/scan
Qi Dou et al [73] 3D convolutional neural
network
Sensitivity 92.2%
No. of FP=8/scan
7. Conclusion
The authors have presented a critical review of literature with regards to CT scans of
lung nodule detection and classification using computer aided diagnosis methods. This
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research review has included research papers published in peer reviewed journals up to
March, 2018. They have been sourced from Web of Science, PubMed, IEEE Xplore, and
Science Direct.
This paper has identified the increased sensitivity of some of the techniques used and the
reduction of false positives when some particular algorithms are used. If the medical
community uses these techniques, they can scan more people quickly and thus, taking the
health care to a wider section of the society. This would benefit nation as a whole since early
detection and diagnosis of lung cancer can save potentially millions of people and extend
their life span. This would in turn, generate precious human resource which would be a great
asset to any nation.
In this regard, there should be a close correlation between the medical professionals and the
engineers who develop various CADe and CADx systems. There should be a proper synergy
between various agencies who are at stake in this process. The authors sincerely believe that
this effort would be a right step in that direction.
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