Development of an automated region of interest selection method for 3D surface monitoring of head motion

Abstract

To simplify the often complex and user-dependent manual region of interest (ROI) selection process for head motion monitoring, an automatic ROI selection method was developed. The automatic ROI selection algorithm calculated the displacements and velocities of 3D surface points between a temporally correlated 3D image series and a reference image. Only facial surfaces satisfying certain spatial and temporal criteria were selected. The algorithm was tested on five healthy volunteers instructed to perform different types of facial movements for a total of 27 real-time image sets (40-120 images for each image set). The algorithm detected and excluded surface areas affected by different types of local facial movements that were independent of actual net head motion. Eye, eyebrow, and mandible motion were most commonly detected as being independent of head motion and were excluded from the final ROI. For 3D images taken with substantial facial or whole head motion, either most of the facial area was excluded or only small areas with random patterns were included in the final ROI. Surface image registration using iterative closest point (ICP) methods showed more stable real-time head tracking using the automatically selected ROI than manual user defined ROIs. The automatic selection method successfully found ROIs stable over time for tracking head motion by excluding locally varying facial motions. By automating the ROI selection process, it is feasible that the time and complexity of current ROI definition can be reduced, together with user-dependent registration errors.