Abstract
Feature tracking for endoscopic images is a critical component for image guided applications in minimally invasive surgery. Recent work in this field has shown success in acquiring tissue deformation, but it still faces issues. In particular, it often requires expensive algorithms to filter outliers. In this paper, we firstly propose two real-time pre-processes based on image filtering, to improve feature tracking robustness and thus reduce outlier percentage. However the performance evaluation of detection and tracking algorithms on endoscopic images is still difficult and not standardized, due to the difficulty of ground truth data acquisition. To overcome this drawback, we secondly propose a novel framework that allows to provide artificial ground truth data, and thus to evaluate detection and feature tracking performances. Finally, we demonstrate, using our framework on 9 different in-vivo video sequences, that the proposed pre-processes significantly increase the tracking performance.
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Selka, F., Nicolau, S.A., Agnus, V., Bessaid, A., Marescaux, J., Soler, L. (2013). Evaluation of Endoscopic Image Enhancement for Feature Tracking: A New Validation Framework. In: Liao, H., Linte, C.A., Masamune, K., Peters, T.M., Zheng, G. (eds) Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions. MIAR AE-CAI 2013 2013. Lecture Notes in Computer Science, vol 8090. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40843-4_9
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DOI: https://doi.org/10.1007/978-3-642-40843-4_9
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