Segmenting nodules of lung tomography image with level set algorithm and neural network
Lung cancer is the main cause of death in the world. It is diagnosed generally by analysing a tissue cluster formation called 'nodule' inside the lung. Computer aided diagnosis (CAD) plays an important role in medical field which helps radiologists to detect and localise lung nodule. The a...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | Proceedings |
Language: | English English |
Published: |
IEEE Inc.
2019
|
Subjects: | |
Online Access: | https://eprints.ums.edu.my/id/eprint/31751/1/Segmenting%20nodules%20of%20lung%20tomography%20image%20with%20level%20set%20algorithm%20and%20neural%20network.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/31751/2/Segmenting%20nodules%20of%20lung%20tomography%20image%20with%20level%20set%20algorithm%20and%20neural%20network.pdf https://eprints.ums.edu.my/id/eprint/31751/ https://ieeexplore.ieee.org/document/9067987 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.ums.eprints.31751 |
---|---|
record_format |
eprints |
spelling |
my.ums.eprints.317512022-02-24T08:44:48Z https://eprints.ums.edu.my/id/eprint/31751/ Segmenting nodules of lung tomography image with level set algorithm and neural network Chong, Soon Yee Tan, Min Keng Yeo, Kiam Beng Mohd Yusof Ibrahim Hao, Xiaoxi Teo, Kenneth Tze Kin QA1-939 Mathematics RC705-779 Diseases of the respiratory system Lung cancer is the main cause of death in the world. It is diagnosed generally by analysing a tissue cluster formation called 'nodule' inside the lung. Computer aided diagnosis (CAD) plays an important role in medical field which helps radiologists to detect and localise lung nodule. The aim of this research is to develop an image segmentation algorithm for nodule detection in computed tomography (CT) image. Performance of the developed segmentation algorithm is analysed through testing on the lung CT image obtained from public online database, Lung Image Database Consortium (LIDC). To segment the lung nodule, preprocessing techniques are applied on the CT image followed by segmentation approaches to segment the nodule inside the lung. In this research, median filter is applied to improve the quality and filter the background noise of the image. Segmentation technique which is level set method (LSM) is applied to segment the nodule. After that, feature extraction process is carried out to obtain the essential information of the segmented nodules for further analysis or classification purpose. Here, the features extracted including centroid, major and minor axis length and area of the nodule. Performance of the segmentation algorithms is analysed using 20 lung CT images from LIDC dataset. Lastly, classification step is done using Artificial Neural Network (ANN) to classify the segmented nodule to different categories. From the analysis, LSM technique performs better in cases of bigger nodule compared to smaller size nodule. The result shows that the proposed segmentation method can effectively segment the lung nodule. IEEE Inc. 2019-12 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/31751/1/Segmenting%20nodules%20of%20lung%20tomography%20image%20with%20level%20set%20algorithm%20and%20neural%20network.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/31751/2/Segmenting%20nodules%20of%20lung%20tomography%20image%20with%20level%20set%20algorithm%20and%20neural%20network.pdf Chong, Soon Yee and Tan, Min Keng and Yeo, Kiam Beng and Mohd Yusof Ibrahim and Hao, Xiaoxi and Teo, Kenneth Tze Kin (2019) Segmenting nodules of lung tomography image with level set algorithm and neural network. https://ieeexplore.ieee.org/document/9067987 |
institution |
Universiti Malaysia Sabah |
building |
UMS Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Malaysia Sabah |
content_source |
UMS Institutional Repository |
url_provider |
http://eprints.ums.edu.my/ |
language |
English English |
topic |
QA1-939 Mathematics RC705-779 Diseases of the respiratory system |
spellingShingle |
QA1-939 Mathematics RC705-779 Diseases of the respiratory system Chong, Soon Yee Tan, Min Keng Yeo, Kiam Beng Mohd Yusof Ibrahim Hao, Xiaoxi Teo, Kenneth Tze Kin Segmenting nodules of lung tomography image with level set algorithm and neural network |
description |
Lung cancer is the main cause of death in the world. It is diagnosed generally by analysing a tissue cluster formation called 'nodule' inside the lung. Computer aided diagnosis (CAD) plays an important role in medical field which helps radiologists to detect and localise lung nodule. The aim of this research is to develop an image segmentation algorithm for nodule detection in computed tomography (CT) image. Performance of the developed segmentation algorithm is analysed through testing on the lung CT image obtained from public online database, Lung Image Database Consortium (LIDC). To segment the lung nodule, preprocessing techniques are applied on the CT image followed by segmentation approaches to segment the nodule inside the lung. In this research, median filter is applied to improve the quality and filter the background noise of the image. Segmentation technique which is level set method (LSM) is applied to segment the nodule. After that, feature extraction process is carried out to obtain the essential information of the segmented nodules for further analysis or classification purpose. Here, the features extracted including centroid, major and minor axis length and area of the nodule. Performance of the segmentation algorithms is analysed using 20 lung CT images from LIDC dataset. Lastly, classification step is done using Artificial Neural Network (ANN) to classify the segmented nodule to different categories. From the analysis, LSM technique performs better in cases of bigger nodule compared to smaller size nodule. The result shows that the proposed segmentation method can effectively segment the lung nodule. |
format |
Proceedings |
author |
Chong, Soon Yee Tan, Min Keng Yeo, Kiam Beng Mohd Yusof Ibrahim Hao, Xiaoxi Teo, Kenneth Tze Kin |
author_facet |
Chong, Soon Yee Tan, Min Keng Yeo, Kiam Beng Mohd Yusof Ibrahim Hao, Xiaoxi Teo, Kenneth Tze Kin |
author_sort |
Chong, Soon Yee |
title |
Segmenting nodules of lung tomography image with level set algorithm and neural network |
title_short |
Segmenting nodules of lung tomography image with level set algorithm and neural network |
title_full |
Segmenting nodules of lung tomography image with level set algorithm and neural network |
title_fullStr |
Segmenting nodules of lung tomography image with level set algorithm and neural network |
title_full_unstemmed |
Segmenting nodules of lung tomography image with level set algorithm and neural network |
title_sort |
segmenting nodules of lung tomography image with level set algorithm and neural network |
publisher |
IEEE Inc. |
publishDate |
2019 |
url |
https://eprints.ums.edu.my/id/eprint/31751/1/Segmenting%20nodules%20of%20lung%20tomography%20image%20with%20level%20set%20algorithm%20and%20neural%20network.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/31751/2/Segmenting%20nodules%20of%20lung%20tomography%20image%20with%20level%20set%20algorithm%20and%20neural%20network.pdf https://eprints.ums.edu.my/id/eprint/31751/ https://ieeexplore.ieee.org/document/9067987 |
_version_ |
1760230933873557504 |
score |
13.160551 |