Abstract
With the development of advanced image acquisition and processing techniques providing better biomarkers for the characterization of brain diseases, the automatic analysis of biomedical imaging constitutes a critical point. In particular, analysis of complex data structure is a challenge for better understanding complex brain pathologies like multiple sclerosis (MS).
In this work, we describe a new fully automated method based on non-negative tensor factorization (NTF) to analyze white matter (WM) fiber-bundles. This method allows to extract, from a WM fiber-bundle, the set of fibers affected by the pathology, discriminating fibers affected by the pathological from the healthy fibers.
Our method was validated on simulated data and also applied on real MS patients. Results show the high precision level of our method to extract fibers affected by the pathological process.
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Acknowledgement
This research was supported by ERC Advanced Grant, #339804 BIOTENSORS and EU MC ITN TRANSACT 2012 #316679.
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Stamile, C., Cotton, F., Maes, F., Sappey-Marinier, D., Van Huffel, S. (2016). White Matter Fiber-Bundle Analysis Using Non-negative Tensor Factorization. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_73
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DOI: https://doi.org/10.1007/978-3-319-41501-7_73
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