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Model-based hybrid variational level set method applied to lung cancer detection

The precise segmentation of lung lesions in computed tomography (CT) scans holds paramount importance for lung cancer research, offering invaluable information for clinical diagnosis and treatment. Nevertheless, achieving efficient detection and segmentation with acceptable accuracy proves to be cha...

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Main Authors: Jing, Wang, Liew, Siau-Chuin, Azian, Abd Aziz
Format: Article
Language:English
Published: Frontier Scientific Publishing 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/41157/1/Model-based%20hybrid%20variational%20level%20set%20method%20applied%20to%20lung%20cancer%20detection.pdf
http://umpir.ump.edu.my/id/eprint/41157/
https://doi.org/10.32629/jai.v7i5.921
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spelling my.ump.umpir.411572024-05-13T07:23:48Z http://umpir.ump.edu.my/id/eprint/41157/ Model-based hybrid variational level set method applied to lung cancer detection Jing, Wang Liew, Siau-Chuin Azian, Abd Aziz QA75 Electronic computers. Computer science The precise segmentation of lung lesions in computed tomography (CT) scans holds paramount importance for lung cancer research, offering invaluable information for clinical diagnosis and treatment. Nevertheless, achieving efficient detection and segmentation with acceptable accuracy proves to be challenging due to the heterogeneity of lung nodules. This paper presents a novel model-based hybrid variational level set method (VLSM) tailored for lung cancer detection. Initially, the VLSM introduces a scale-adaptive fast level-set image segmentation algorithm to address the inefficiency of low gray scale image segmentation. This algorithm simplifies the (Local Intensity Clustering) LIC model and devises a new energy functional based on the region-based pressure function. The improved multi-scale mean filter approximates the image’s offset field, effectively reducing gray-scale inhomogeneity and eliminating the influence of scale parameter selection on segmentation. Experimental results demonstrate that the proposed VLSM algorithm accurately segments images with both gray-scale inhomogeneity and noise, showcasing robustness against various noise types. This enhanced algorithm proves advantageous for addressing real-world image segmentation problems and nodules detection challenges. Frontier Scientific Publishing 2024 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/41157/1/Model-based%20hybrid%20variational%20level%20set%20method%20applied%20to%20lung%20cancer%20detection.pdf Jing, Wang and Liew, Siau-Chuin and Azian, Abd Aziz (2024) Model-based hybrid variational level set method applied to lung cancer detection. Journal of Autonomous Intelligence, 7 (5). pp. 1-14. ISSN 2630-5046. (Published) https://doi.org/10.32629/jai.v7i5.921 10.32629/jai.v7i5.921
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Jing, Wang
Liew, Siau-Chuin
Azian, Abd Aziz
Model-based hybrid variational level set method applied to lung cancer detection
description The precise segmentation of lung lesions in computed tomography (CT) scans holds paramount importance for lung cancer research, offering invaluable information for clinical diagnosis and treatment. Nevertheless, achieving efficient detection and segmentation with acceptable accuracy proves to be challenging due to the heterogeneity of lung nodules. This paper presents a novel model-based hybrid variational level set method (VLSM) tailored for lung cancer detection. Initially, the VLSM introduces a scale-adaptive fast level-set image segmentation algorithm to address the inefficiency of low gray scale image segmentation. This algorithm simplifies the (Local Intensity Clustering) LIC model and devises a new energy functional based on the region-based pressure function. The improved multi-scale mean filter approximates the image’s offset field, effectively reducing gray-scale inhomogeneity and eliminating the influence of scale parameter selection on segmentation. Experimental results demonstrate that the proposed VLSM algorithm accurately segments images with both gray-scale inhomogeneity and noise, showcasing robustness against various noise types. This enhanced algorithm proves advantageous for addressing real-world image segmentation problems and nodules detection challenges.
format Article
author Jing, Wang
Liew, Siau-Chuin
Azian, Abd Aziz
author_facet Jing, Wang
Liew, Siau-Chuin
Azian, Abd Aziz
author_sort Jing, Wang
title Model-based hybrid variational level set method applied to lung cancer detection
title_short Model-based hybrid variational level set method applied to lung cancer detection
title_full Model-based hybrid variational level set method applied to lung cancer detection
title_fullStr Model-based hybrid variational level set method applied to lung cancer detection
title_full_unstemmed Model-based hybrid variational level set method applied to lung cancer detection
title_sort model-based hybrid variational level set method applied to lung cancer detection
publisher Frontier Scientific Publishing
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41157/1/Model-based%20hybrid%20variational%20level%20set%20method%20applied%20to%20lung%20cancer%20detection.pdf
http://umpir.ump.edu.my/id/eprint/41157/
https://doi.org/10.32629/jai.v7i5.921
_version_ 1822924312078712832
score 13.244413

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