A Novel Tracheal Segmentation in Video Endoscopy Using Crayfish Optimization and 2D Non-Local Mean Filtering

Adel Oulefki, Amir Hamza, Abbes Amira, Fatih Kurugollu, Yassine Himeur

Abstract: Tracheal intubation is an essential procedure for maintaining airway patency in patients, but is associated with significant risks, such as improper placement of the tube. Recent advances in video laryngoscopy have improved intubation safety by allowing precise tracheal segmentation in endoscopic images. However, many existing segmentation methods are either computationally intensive or fail to deliver robust results. In this work, we propose a novel multilevel thresholding approach that combines the statistical power of Kapur entropy with the Crayfish Optimization Algorithm (COA) to derive optimal thresholds from a two-dimensional histogram constructed using nonlocal mean filtering. Our method, termed 2DNLM-COA, achieves effective pixel separation and noise reduction while preserving critical edge features, thus facilitating improved identification of the tracheal region. Extensive experiments on video endoscopy data sets demonstrate that our approach outperforms several state-of-the-art methods. In particular, our method achieves a PSNR of 27.87, an SSIM of 0.9194, and an FSIM of 0.9080, indicating its strong potential to improve clinical intubation procedures.

External IDs:dblp:conf/euvip/OulefkiHAKH25