期刊
APPLIED SCIENCES-BASEL
卷 10, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/app10072346
关键词
automated seeded region growing; 3D chain code; firefly; lung cancer; pulmonary nodule; random forest
类别
资金
- ASEAN University Network/Southeast Asia Engineering Education Development Network (AUN/SEED-Net)
- Elsevier copyrights team
The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.
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