Abstract
In this paper we discuss the impact of using algorithms for dictionary learning to build adaptive and sparse representations of medical images. The effectiveness of coding data as sparse linear combinations of the elements of an over-complete dictionary is well assessed in the medical context. Confirming what has been observed for natural images, we show the benefits of using adaptive dictionaries, directly learned from a set of training images, that better capture the distribution of the data. The experiments focus on the specific task of image denoising and produce clear evidence of the benefits obtained with the proposed approach.
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Staglianò, A., Chiusano, G., Basso, C., Santoro, M. (2011). Learning Adaptive and Sparse Representations of Medical Images. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2010. Lecture Notes in Computer Science, vol 6533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18421-5_13
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DOI: https://doi.org/10.1007/978-3-642-18421-5_13
Publisher Name: Springer, Berlin, Heidelberg
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