An unsupervised metaheuristic search approach for segmentation and volume measurement of pulmonary nodules in lung CT scans
作者：
Highlights：
• Diagnosis of pulmonary nodules is fundamental to improve the survival rate of patients.
• Accurate segmentation leads to measure a nodule volume or characterize its morphology properly.
• Proposing a new method to automatically segment and measure the volume of pulmonary nodules.
• Studying on the metaheuristic search based on evolutionary computation.
• Results validation on the LIDC-IDRI dataset.
摘要
•Diagnosis of pulmonary nodules is fundamental to improve the survival rate of patients.•Accurate segmentation leads to measure a nodule volume or characterize its morphology properly.•Proposing a new method to automatically segment and measure the volume of pulmonary nodules.•Studying on the metaheuristic search based on evolutionary computation.•Results validation on the LIDC-IDRI dataset.
论文关键词：Pulmonary nodules,Segmentation,Volume measurement,Clustering,Metaheuristic search,Evolutionary computation
论文评审过程：Received 9 June 2018, Revised 5 November 2018, Accepted 6 November 2018, Available online 9 November 2018, Version of Record 14 November 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.11.010
