Abstract
The enhancement and detection of elongated structures in noisy image data is relevant for many biomedical applications. To handle complex crossing structures in 2D images, 2D orientation scores \(U: \mathbb {R} ^ 2\times S ^ 1 \rightarrow \mathbb {R}\) were introduced, which already showed their use in a variety of applications. Here we extend this work to 3D orientation scores \(U: \mathbb {R} ^ 3 \times S ^ 2\rightarrow \mathbb {R}\). First, we construct the orientation score from a given dataset, which is achieved by an invertible coherent state type of transform. For this transformation we introduce 3D versions of the 2D cake-wavelets, which are complex wavelets that can simultaneously detect oriented structures and oriented edges. For efficient implementation of the different steps in the wavelet creation we use a spherical harmonic transform. Finally, we show some first results of practical applications of 3D orientation scores.
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Janssen, M., Duits, R., Breeuwer, M. (2015). Invertible Orientation Scores of 3D Images. In: Aujol, JF., Nikolova, M., Papadakis, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2015. Lecture Notes in Computer Science(), vol 9087. Springer, Cham. https://doi.org/10.1007/978-3-319-18461-6_45
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DOI: https://doi.org/10.1007/978-3-319-18461-6_45
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