Abstract
Endoscopic procedures provide important information about the internal patient anatomy but are currently restricted to a 2D texture analysis of the visible organ surfaces. Spectral imaging has high potential in generating valuable complementary information about the molecular tissue composition but suffers from long image acquisition times. As the technique requires an aligned stack of images, its benefit in endoscopic procedures is still very limited due to continuous motion of the camera and the tissue. In this paper, we present an information theory based approach to band selection for endoscopic spectral imaging. In contrast to previous approaches, our concept does not require labelled training data or an elaborate light-tissue interaction model. According to a validation study using phantom data as well as in vivo spectral data obtained from five surgeries in a porcine model, only few bands selected by our method are sufficient to reconstruct the tissue composition with a similar accuracy as obtainable with the full spectrum.
Sebastian J. Wirkert, and Neil T. Clancy contributed equally to this work.
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Wirkert, S.J. et al. (2014). Endoscopic Sheffield Index for Unsupervised In Vivo Spectral Band Selection. In: Luo, X., Reichl, T., Mirota, D., Soper, T. (eds) Computer-Assisted and Robotic Endoscopy. CARE 2014. Lecture Notes in Computer Science(), vol 8899. Springer, Cham. https://doi.org/10.1007/978-3-319-13410-9_11
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DOI: https://doi.org/10.1007/978-3-319-13410-9_11
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