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)
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Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | lung cancer; medical image; computed tomography |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Computing |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 13 May 2024 07:23 |
| Last Modified: | 13 May 2024 07:23 |
| URI: | http://umpir.ump.edu.my/id/eprint/41157 |
| Download Statistic: | View Download Statistics |
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