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: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontier Scientific Publishing
2024
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| Subjects: | |
| 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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| Summary: | 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. |
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