Spatio-temporal Image Tracking Based on Optical Flow and Clustering: An Endoneurosonographic Application

Abstract

On the process of render brain tumors from endoneurosonography, one of the most important steps consists in track the axis line of an ultrasound probe throughout successive endoscopic images. Recognizing of this line is important because it allows computing its 3D coordinates using the projection matrix of the endoscopic cameras. In this paper we present a method to track an ultrasound probe in successive endoscopic images without relying on any external tracking system. The probe is tracked using a spatio-temporal technique based on optical flow and clustering algorithm. Firstly, we compute the optical flow using the Horn-Schunck algorithm. Secondly, a feature space using the optical flow magnitude and luminance is defined. Thirdly, feature space is partitioned in two regions using the k-means clustering algorithm. After this, we calculate the axis line of the ultrasound probe using Principal Component Analysis (PCA) over segmented region. Finally, a motion restriction is defined over consecutive frames in order to avoid tracking errors. We have used endoscopic images from brain phantoms to evaluate the performance of the proposed method, we compare our methodology against ground truth and a based–color particle filter, and our results show that it is robust and accurate.