In intensive care units (ICU), endotracheal (ET) tubes are inserted to assist patients who may have difficulty breathing.
A malpositioned ET tube could lead to a collapsed lung, which is life threatening. The purpose of this study is to
develop a new method that automatically detects the positioning of ET tubes on portable chest X-ray images. The
method determines a region of interest (ROI) in the image and processes the raw image to provide edge enhancement for
further analysis. The search of ET tubes is performed within the ROI. The ROI is determined based upon the analysis of
the positions of the detected lung area and the spine in the image. Two feature images are generated: a Haar-like image
and an edge image. The Haar-like image is generated by applying a Haar-like template to the raw ROI or the enhanced
version of the raw ROI. The edge image is generated by applying a direction-specific edge detector. Both templates are
designed to represent the characteristics of the ET tubes. Thresholds are applied to the Haar-like image and the edge
image to detect initial tube candidates. Region growing, combined with curve fitting of the initial detected candidates, is
performed to detect the entire ET tube. The region growing or "tube growing" is guided by the fitted curve of the initial
candidates. Merging of the detected tubes after tube growing is performed to combine the detected broken tubes. Tubes
within a predefined space can be merged if they meet a set of criteria. Features, such as width, length of the detected
tubes, tube positions relative to the lung and spine, and the statistics from the analysis of the detected tube lines, are
extracted to remove the false-positive detections in the images. The method is trained and evaluated on two different
databases. Preliminary results show that computer-aided detection of tubes in portable chest X-ray images is promising.
It is expected that automated detection of ET tubes could lead to timely detection of malpositioned tubes, thus improve
overall patient care.