CT lung images segmentation using image processing and Markov random field
Introduction: In this study, the performance of computed tomography lung image segmentation using image processing and Markov Random Field was investigated. Before cancer segmentation and analysis, lung segmentation is an important initial process. Thus, the aim of this study is to find the optimal...
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Universiti Putra Malaysia Press
2022
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my.utem.eprints.267732023-04-14T14:54:37Z http://eprints.utem.edu.my/id/eprint/26773/ CT lung images segmentation using image processing and Markov random field A Aziz, Khairul Azha Saripan, M. Iqbal Raja Abdullah, Raja Syamsul Azmir Ahmad Saad, Fathinul Fikri Introduction: In this study, the performance of computed tomography lung image segmentation using image processing and Markov Random Field was investigated. Before cancer segmentation and analysis, lung segmentation is an important initial process. Thus, the aim of this study is to find the optimal Markov Random Field setting for lung segmentation. Methods: The Centre for Diagnostic Nuclear Imaging at UPM provided 11 anonymous sets of cancerous lung CT images for this study. The thresholding technique is an effective method for medical image segmentation when the priori information for the region of interest is known, such as the Hounsfield Unit value of lung. Due to the large differences in grey levels in the image, the thresholding approach is difficult to apply in segmentation, especially for lung. Thus, for the segmentation process, this study used multilevel thresholding with Markov Random Field with three settings; Iterated Condition Mode, Metropolis algorithm, and Gibbs sampler. The images then went through image processing procedures which were binarization, small object removal, lung region extraction and lung segmentation. The output from the experiments were analyzed and compared to determine the ideal lung segmentation setting. Results: The Jaccard index average values; Markov Random Field -Metropolis = 0.9464, Markov Random Field -ICM = 0.9499 and Markov Random Field -Gibbs = 0.9512. The Dice index average values; Markov Random Field - Metropolis = 0.9743, Markov Random Field - ICM = 0.9724 and Markov Random Field - Gibbs = 0.9749. Conclusion: Markov Random Field using Gibbs sampler delivered the best results for lung segmentation. Universiti Putra Malaysia Press 2022-04 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26773/2/2022042811255706_MJMHS_0677.PDF A Aziz, Khairul Azha and Saripan, M. Iqbal and Raja Abdullah, Raja Syamsul Azmir and Ahmad Saad, Fathinul Fikri (2022) CT lung images segmentation using image processing and Markov random field. Malaysian Journal of Medicine and Health Sciences, 18 (SUPP 6). pp. 31-35. ISSN 1675-8544 https://medic.upm.edu.my/upload/dokumen/2022042811255706_MJMHS_0677.pdf 10.47836/mjmhs.18.s6.6 |
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Introduction: In this study, the performance of computed tomography lung image segmentation using image processing and Markov Random Field was investigated. Before cancer segmentation and analysis, lung segmentation is an important initial process. Thus, the aim of this study is to find the optimal Markov Random Field setting for lung segmentation.
Methods: The Centre for Diagnostic Nuclear Imaging at UPM provided 11 anonymous sets of cancerous lung CT images for this study. The thresholding technique is an effective method for medical image segmentation when the priori information for the region of interest is known, such as the Hounsfield Unit value of lung. Due to the large differences in grey levels in the image, the thresholding approach is difficult to apply in segmentation, especially for lung. Thus, for the segmentation process, this study used multilevel thresholding with Markov Random Field with three settings; Iterated Condition Mode, Metropolis algorithm, and Gibbs sampler. The images then went through image processing procedures which were binarization, small object removal, lung region extraction and lung segmentation. The output from the experiments were analyzed and compared to determine the ideal lung segmentation setting.
Results: The Jaccard index average values; Markov Random Field -Metropolis = 0.9464, Markov Random Field -ICM = 0.9499 and Markov Random Field -Gibbs = 0.9512. The Dice index average values; Markov Random Field - Metropolis = 0.9743, Markov Random Field - ICM = 0.9724 and Markov Random Field - Gibbs = 0.9749.
Conclusion: Markov Random Field using Gibbs sampler delivered the best results for lung segmentation. |
format |
Article |
author |
A Aziz, Khairul Azha Saripan, M. Iqbal Raja Abdullah, Raja Syamsul Azmir Ahmad Saad, Fathinul Fikri |
spellingShingle |
A Aziz, Khairul Azha Saripan, M. Iqbal Raja Abdullah, Raja Syamsul Azmir Ahmad Saad, Fathinul Fikri CT lung images segmentation using image processing and Markov random field |
author_facet |
A Aziz, Khairul Azha Saripan, M. Iqbal Raja Abdullah, Raja Syamsul Azmir Ahmad Saad, Fathinul Fikri |
author_sort |
A Aziz, Khairul Azha |
title |
CT lung images segmentation using image processing and Markov random field |
title_short |
CT lung images segmentation using image processing and Markov random field |
title_full |
CT lung images segmentation using image processing and Markov random field |
title_fullStr |
CT lung images segmentation using image processing and Markov random field |
title_full_unstemmed |
CT lung images segmentation using image processing and Markov random field |
title_sort |
ct lung images segmentation using image processing and markov random field |
publisher |
Universiti Putra Malaysia Press |
publishDate |
2022 |
url |
http://eprints.utem.edu.my/id/eprint/26773/2/2022042811255706_MJMHS_0677.PDF http://eprints.utem.edu.my/id/eprint/26773/ https://medic.upm.edu.my/upload/dokumen/2022042811255706_MJMHS_0677.pdf |
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