Automatic detection of region of interest based on object tracking in neurosurgical video

Automatic detection of region of interest (ROIs) in a complex image or video, such as an angiogram or endoscopic neurosurgery video, is a critical task in many medical image and video processing applications. In this paper, we present a new method that addresses several challenges in automatic detection of ROI of neurosurgical video for ROI coding which is used for neurophysiological intraoperative monitoring (IOM) system. This method is based on an object tracking technique with multivariate density estimation theory, combined with the shape information of the object. By defining the ROIs for neurosurgical video, this method produces a smooth and convex emphasis region within which surgical procedures are performed. A large bandwidth budget is assigned within the ROI to archive high-fidelity Internet transmission. Outside the ROI, a small bandwidth budget is allocated to efficiently utilize the bandwidth resource. We believe this method also can be used to image-guiduance surgery (IGS) systems to track the positions of surgical instruments in the physical space occupied by the patient after some improvement.