IRJET- Lung Segmentation and Nodule Detection based on CT Images using Image Processing Method

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 02 | Feb 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 487
Lung Segmentation and Nodule Detection based on CT Images using
Image Processing Method
Mr. Shailesh S. Bhise1, Prof. S. R. Khot2
1P.G.Student,Electronics & Telecommunication Department, D. Y. Patil College of Engineering and Technology,
Kolhapur (MS), India
2Associate Professor, Electronics & Telecommunication Department, D. Y. Patil College of Engineering and
Technology, Kolhapur (MS), India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Lung nodule detection and segmentation is
important for clinical diagnosis. Standard Computer Aided
Diagnosis (CAD) systems for Lung cancer detection should
employ four steps: preprocessing, lungs segmentation, nodule
detection and reduction of False Positives (FP). This paper
proposes a lung nodule detection and segmentation method
based on a region growing method, circle fit algorithm and
other image processing techniques. In the proposed approach
during the preprocessing step, several masks are calculated
using thresholding technique and morphological operations,
eliminating this way, background and surrounding tissue.
Following, Regions of Interest (ROI) are calculated using a
priori information and Hounsfield Units (HU). During feature
extraction, numerous features are calculated in order to
restrict the suspicious zones. Finally, ArtificialNeuralNetwork
(ANN) algorithm is employed in classification stage.
Key Words: CAD; CT Image.; Lung Nodule; ANN
1. INTRODUCTION
Lung cancer is common due to smoking and it is mainly
caused by uncontrollable irregular growth of cells in lung
tissue. If it is detected earlier, then it is betteristhechance of
curing. For lung cancer detection, one of the most important
and fundamental step is screening. Screening is the process
used for identification of nodule. A nodule is a white color
spot present on lungs that is visible onanX-rayorComputed
Tomography (CT) scans images. A nodule may be of two
types: Either a benign or a mass. A nodule that is 3 cm orless
in diameter is called a Pulmonary or Benign nodule. These
types of nodule are noncancerous. Pulmonary nodules are
the characterization of early stage of lung cancer. Another
type of nodule whose size is larger than 3 cm is in diameter
is called as a lung mass. This type of nodule is likely to be
cancerous and needs to be detected as early as possible.
These nodules need to be followed over time to check if they
are growing. The larger the nodule more is its possibility of
being cancer. Thus, a nodule needs to be under observation.
Most of the nodules which are noncancerous have a very
smooth or round margin.
The survival rate of lung cancer is very low when compared
with all other types of cancer. The need for identifying lung
cancer at an early stage is very essential and is an active
research area in the field of medical image processing.
2. RELATED WORK
Madhura J et al [ICIMIA] [2017] [1]: Author has described
the different types of noise in medical imagingand explained
the different techniques for the removal of noise. Detection
of a nodule is fundamental problem in medical image
processing. According to Kostis, W.J., Reeves, A.P.,
Yankelevitz, D.F. [2], there 4 types of nodules. (i). Well-
circumscribed: In this case, the nodules are not connectedto
vasculature but are at the core of the lung tissue. (ii). Juxta -
vascular: In this case, the nodules are at the centre of the
lung field and are connected to the surroundinglungvessels.
(iii). Pleural Tail: These types of nodule are connected by a
thin structure and are located near the pleural surface. (iv).
Juxta-pleural: Here a thin structure is connected by the
substantial portion of the nodule. Qing Wu and Wenbing
Zhao (ISCSIC) [2017] [3] : Author has proposed a novel
neural-network based algorithm, which they refer as
entropy degradation method (EDM), to detect small celllung
cancer (SCLC) from computed tomography (CT) images for
early cancer prediction. Rachid Sammouda (KACST) [2016]
[4] :Author has developedan automaticCADsystemforearly
detection of lung cancer for that purpose they analyzed lung
human CT images using several phases&theapproachstarts
by extracting the lung regions from the CT image using
classical image processing techniques, including bit-planes
representation of raw 3D-CT images producing 2D slices.
They have applied various procedures, Erosion, Median
filter, Dilation, Outlining, Lung Border Extraction and Flood
Fill algorithm, in sequence.
However, due to the number of patients increasing day by
day it is the workload of radiologists who need to analyze
the tests in a short time is also increasing. Due to this, the
radiologists may misinterpret causing errors in detection.
Therefore, CAD systems that can detect nodules efficiently
and effectively within a short duration of time is needed [5].
The two main CAD systems used byradiologists to assist
them, they are: CADe– These systems are used onlytodetect
a tumor. CADx– Theses are used to check the characteristics
of a tumor. Nanusha [6] proposed an approach is
quantitative surface characterization of pulmonary nodules
based on thin section CT images. In this approach describes
segmentation of the three-dimensional (3D) nodule images
are obtained by a 3D deformable surfaces approach.

3. PROPOSED METHOD
The proposed system consist of three modules such as pre-
processing the CT chest image, segmentation of lung region,
extraction of lung nodule candidates and classification of
nodules. This can be shown in Figure 1.
3.1 Pre-processing
The pre-processing is done before the main data is
processed. The main objective of pre-processing is to
improve the quality of the image that may be corrupted due
to noise during data acquisition.To separatethe background
noise, it is required to pre-process the images. It is mainly to
enhance the quality of data through the application of
methods for denoising. [9]. Some of the important
techniques used fordata pre-processingareMedianFiltering
[4][5], Histogram Equalization [5], Fast Fourier Transform
[6]
Fig-1: Flow Diagram of lung nodule detection
3.2 Segmentation of lungs
Image segmentation is processofpartitioninga digital image
into multiple segments. So the goal of segmentation is to
simplify or change the representation of an image into
something that is more meaningful and easier to analyse.
Region based segmentation is used to find region of interest
(ROI) and segmented for further processing [4]. Region
based methods have the purpose of grouping pixels having
similar intensities. Region based segmentation follows this
basic procedure as follows:
i) For region-based lung segmentation, the “seeded” scheme
is commonly applied. In such cases,small patch(seed)thatis
considered to be most representative of the target region
(lung) is first identified.
ii) Seed points are the coordinates of a representative set of
points belonging to the target organ to be segmented, and
they can be selected either manually or automatically.
iii) Once the seed points are identified, a predefined
neighbourhood criterion is used to extract the desired
region. Different methods, features are usedfordetermining
the lung boundaries. For instance, one of possible criterion
could be to grow the region until the lung edge is detected.
3.3 Extract Nodules
Before extracting desired nodules, image enhancement pre-
processing is done again. Some of the important techniques
used for data pre-processing are image background, gray
Thresholding for binarization and image boundary
connected objects are cleared.
Then desired nodule with area greater than minimum area
and less than maximum area is segmented.
Using circle fit algorithm with maximum radius a nodule is
detected with desired area.
3.4 Classification and Detection
Nodule detection is the most important step in the detection
of lung cancer. After the nodule detection,the nextstepisthe
classification of the nodule as benign or malignant. Most of
the pulmonary nodules are benign but may represent an
early stage of lung cancer. If a malignant nodule is detected
at an early stage the survival rate of the diseased may
increase. Nodule classification involves assigning pathology
to the detected and isolated nodules.Thisistheultimate goal
of computerized nodule detection for early detection of
doubtful nodules.
4. EXPERIMENTAL RESULTS
First image is selected then lung is extracted
Fig-1: Select Image
Extracted lung Region is obtained using region growing
method. Then applying lung mask proper lung is extracted.
Fig-2: Extracted Lung Mask and Region

Fig-3: Image Enhancement and Nodule Segmentation
Fig-4: Nodule Detection and Selection and Extract Desired
Nodule
Table -1: Sample Data
Nodule
#
Radius Mean
Intensity
Area Euler
Number
ECD
# 1 5.4 940.8 482 1 24.8
# 2 2.8 1530.9 106 1 11.6
5. CONCLUSION
Lung cancer is one of the most harmful diseasesintheworld.
There is a need of proper diagnosis and earlystagedetection
of lung cancer which will increase the survival rate of the
patient. Computer Aided Diagnosis (CAD) involving Image
Processing techniques for nodule detection helps in the
diagnosis of cancer. In this paper, region growingalgorithms
is implemented to segment lung and circle fit algorithm to
detect nodules in lungs from a CT Scan image of Lungs.Itcan
obtain accurate and effective result of pulmonary nodule
detection based on CT images.
REFERENCES
1. Madhura J , Dr .Ramesh Babu D R “A Survey on Noise
Reduction Techniques for Lung Cancer Detection”
International Conference on InnovativeMechanismsfor
Industry Applications(ICIMIA2017),978-1-5090-5960-
7/17/$31.00 ©2017 IEEE
2. Kostis, W.J., Reeves, A.P., Yankelevitz, D.F., et al.,
“Threedimensional segmentation and
growthrateestimation of small pulmonary nodules in
helical CT images”, IEEE Trans., Medical Imaging 22,
pp.1259–1274 ,2003
3. Qing Wu and Wenbing Zhao “Small-Cell Lung Cancer
Detection Using a Supervised Machine Learning
Algorithm” 2017International SymposiumonComputer
Science and Intelligent Controls (ISCSIC) 978-1-5386-
2941-3/17 $31.00 © 2017 IEEE
4. RachidSammouda “Segmentation and Analysis of CT
Chest Images for Early Lung Cancer Detection” 2016
Global Summit on Computer & Information Technology
978-1-5090-2659-3/17 $31.00 © 2017 IEEE
5. Laniketbombale, C.G.Patil ,“Segmentationoflungnodule
in ct data using k-mean clustering”,international journal
of electrical, electronics and data communication, issn:
2320-2084 vol-5, issue-2, feb.-2017
6. Nanusha, “Lung Nodule Detection Using Image
Segmentation Methods”, International Journal of
Advanced Research in Electronics and Communication
Engineering (IJARECE) Volume 6, Issue 7, July 2017
7. Imran FareedNizami,SaadUlHasan,IbrahimTariqJaved,
“A Wavelet Frames + K-means based AutomaticMethod
for Lung Area Segmentation in MultipleSlices ofCT Scan
“ISBN:978-1-4799-5754-5/14/$26.00©2014IEEE245
8. Raghuraman, G., J. P. Ananth, K. L. Shunmuganathan,and
L. Sairamesh. "Krawtchouk Moment Based Interactive
Image Retrieval Algorithm." Journal of Computational
and Theoretical Nanoscience, vol. 12, no. 12, pp. 5562-
5565, 2015.
9. G Raghuraman, S Sabena, and L Sairamesh, “Image
Retrieval Using Relative Location of Multiple ROIS,”
Asian Journal of Information Technology., vol. 15, no. 4,
pp. 772–775, 2016.
10. Ashwin S, Kumar SA, Ramesh J, Gunavathi K: Efficient
and reliable lung nodule detection using a
neuralnetwork based computer aideddiagnosissystem.
In Emerging Trends in Electrical Engineering and
EnergyManagement (ICETEEEM), 2012 International
Conference 2012:135–142.
11. Ye X, Lin X, Dehmeshki J, Slabaugh G, Beddoe G: Shape
based computer-aided detection of lung nodules
inthoracic CT images. Biomed Eng IEEE Trans 2009,
56(7):1810–1820.
12. Arimura H, Magome T, Yamashita Y, Yamamoto D:
Computer-aided diagnosis systems for brain diseases
inmagnetic resonance images. Algorithms 2009,
2(3):925– 952.

13. Q. Wang, W. Kang, C. Wu, B. Wang, Computer-aided
detection of lung nodules by SVM based on 3D matrix
patterns, vol. 37, No.1, pp.62-69, 2013.
14. A. El-Baz, A. Elnakib, M. Abou El-Ghar, G. Gimel'farb, R.
Falk,A. Farag, Automatic Detection of 2D and 3D Lung
Nodules in ChestSpiral CTScans,International Journal of
Biomedical Imaging 2013 Article ID 517632, 11 pages.
doi:10.1155/2013/517632.
15. B. Chen, T. Kitasaka, H. Honma, H. Takabatake, M. Mori,
H. Natori, K. Mori, Automatic segmentation of
pulmonary blood vessels and nodules based on local
intensity structure analysis and surface propagation in
3D chest CT images, vol.7, No.3, pp.465-482, 2012.
16. A. Riccardi, T. S. Petkov, G. Ferri, M. Masotti, R.
Campanini, Computer-aided detection of lung nodules
via 3D fast radial transform, scale space representation,
and Zernike MIP classification, vol.38, No.4, pp. 1962-
1971, 2011.
17. S. Matsumoto, Y. Ohno, H. Yamagata, D. Takenaka,
K.Sugimura, Computer-aided detection of lung nodules
on multi-detector row computed tomography using
three-dimensional analysis of nodule candidates and
their surroundings, vol. 26, No.9, pp. 562-569, 2008.
18. AwaisMansoorPh.D et al, “Segmentation and Image
Analysis of Abnormal Lungs at CT: Current Approaches,
Challenges, and Future Trends”, Radio Graphics 2015;
35:1056–1076 Published online
10.1148/rg.2015140232
19. Salsabil Amin El-Regaily et al, “Lung Nodule
Segmentation and Detection inComputed Tomography”
The 8th IEEE International Conference on Intelligent
Computing and Information Systems(ICICIS2017)978-
1-5386-0821-0/17/$31.00 ©2017 IEEE
20. World Health Organisation.Cancer: fact Sheet no. 297.
2015 July 8.
http://www.who.int/mediacentre/factsheets/fs297/en
/.Jemal A, Siegel R, Xu J, et al. Cancer statistics, 2015.. CA
Cancer J Clin.2015; 60(5):277–300.
21. Goldstraw P, Chansky K, Crowley J, et al. The IASLC lung
cancer staging project: proposals for revision of the
TNM stage groupingsintheforthcoming(eighth)edition
of the tnm classification for lung cancer. J
ThoracOncol.2016;11(1):39–51. IEEE
22. The diagnosis of lung cancer (update) Published by the
National Collaborating Centre for Cancer (2nd Floor,
Front Suite, Park House, Greyfriars Road, Cardiff, CF10
3AF) at Velindre NHS Trust, Cardiff, Wales[2011]
Database from: The Cancer Imaging Archive.
http://doi.org/10.7937/K9/TCIA.2015.A6V7JIWX
BIOGRAPHIES
PG Student, Electronics &
Telecommunication Department, D.
Y. Patil College of Engineering and
Technology, Kolhapur (MS),India.
Working as a I/C HOD-E & TC Engg.,
Latthe Polytechnic, Sangli
Associate Professor, Electronics &
Telecommunication Department, D.
Y. Patil College of Engineering and
Technology, Kolhapur (MS),India.
Specialization: Image Processing.

IRJET- Lung Segmentation and Nodule Detection based on CT Images using Image Processing Method

More Related Content

What's hot (20)

Similar to IRJET- Lung Segmentation and Nodule Detection based on CT Images using Image Processing Method (20)

More from IRJET Journal (20)

Recently uploaded (20)

IRJET- Lung Segmentation and Nodule Detection based on CT Images using Image Processing Method