Dror Lederman, Holon Institute of Technology, Israel

This paper addresses the problem of classification of upper airways images for endotracheal intubation verification in order to improve the safety of patients undergoing general anaesthesia. The proposed method is based on textural features utilized in a continuous probabilistic framework using parallel Gaussian mixture models (GMMs). The classification decision is made based on a maximum likelihood approach, which is insensitive to the angle at which the image was taken. Evaluation of the proposed approach is done using a dataset of 200 images that includes three classes of anatomical structures of the upper airways. The results show that the approach can be used to efficiently and reliably represent and classify medical images acquired during various procedures.

Artificial intelligence, intubation verification, Gaussian mixture models, textural features.