Dynamic MR Visualization and Detection of Upper Airway Obstruction during Sleep Using Region-Growing Segmentation

Obstructive Sleep Apnea(OSA) syndrome is a common breathing disorder in which the airflow pauses during sleep due to physical collapse of pharyngeal airway. Recently, dynamic MRI of the upper airway has been demonstrated during natural sleep, with sufficient fourth dimensional resolution not tending to spread the study patterns of airway obstruction in adults with OSA. In this project, we use open source multi-seeded region growing segmentation approach, where the operator selects a region of interest that includes the pharyngeal airway, by placing three seeds in the patent airway, and determines a threshold for the first frame in order to determine the collapsed tissues which blocks the airway path. This simple automated segmentation technique enhances the process of dynamic MRI studies of the pharyngeal airway and enables diagonising of obstructive events with the collapse plot. Region growing results well in Cosine Coefficients compared with manual segmentation of 1. It automatically detect 90% of collapse events. This approach leads to segment the airway path efficiently. It uses long MRI scans in order to diagnosis the collapsed events with brief, accurate details in a short span of time.