Abstract
Segmentation of noisy hyper-spectral imaging data using clustering requires special algorithms. Such algorithms should consider spatial relations between the pixels, since neighbor pixels should usually be clustered into one group. However, in case of large spectral dimension (p), cluster algorithms suffer from the curse of dimensionality and have high memory requirements as well as long run-times. We propose to embed pixels from a window of w ×w pixels to a feature space of dimension pw 2. The effect of implicit denoising due to the window is controlled by weights depending on the spatial distance. We propose either using Gaussian weights or data-adaptive weights based on the similarity of pixels. Finally, any vectorial clustering algorithm, like k-means, can be applied in this feature space. Then, we use the FastMap algorithm for dimensionality reduction. The proposed algorithm is evaluated on a large simulated imaging mass spectrometry dataset.
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Acknowledgements
The authors gratefully acknowledge the financial support of the European Union Seventh Framework Programme (project “UNLocX”, grant 255931).
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Kobarg, J.H., Alexandrov, T. (2013). Efficient Spatial Segmentation of Hyper-spectral 3D Volume Data. In: Lausen, B., Van den Poel, D., Ultsch, A. (eds) Algorithms from and for Nature and Life. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-00035-0_9
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DOI: https://doi.org/10.1007/978-3-319-00035-0_9
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