Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

Applied Sciences

Chuchart Pintavirooj¹  MayPhu Paing¹  Supan Tungjitkusolmun¹  Sarinporn Visitsattapongse¹  Kazuhiko Hamamoto² 

[1] Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand;School of Information and Telecommunication Engineering, Tokai University, Tokyo 108-8619, Japan;

关键词: automated seeded region growing;    3D chain code;    firefly;    lung cancer;    pulmonary nodule;    random forest;   

DOI  :  10.3390/app10072346

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown