Abstract
Intraoperative thermography allows fast capturing of small temperature variations during neurosurgical operations. External influences induce periodic vibrational motion to the whole camera system superimposing signals of high-frequent neuronal activity, heart rate activity and injected perfusion tracers by motion artifacts. In this work, we propose a robust method to eliminate the effects induced by the vibrational motion allowing further inference of clinical information. For this purpose, an efficient wavelet shrinkage scheme is developed based on subspace analysis in 1D wavelet domain to recognize and remove motion related patterns. The approach does not require any specific motion modeling or image warping, making it fast and preventing image deformations. Promising results of a simulation study and by intraoperative measurements make this method a reliable and efficient method improving subsequent perfusion and neuronal activity analysis.
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Hoffmann, N. et al. (2014). Wavelet Subspace Analysis of Intraoperative Thermal Imaging for Motion Filtering. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_46
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DOI: https://doi.org/10.1007/978-3-319-11755-3_46
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