Abstract
Doctors and researchers in the medical field rely heavily on the availability of accurate images of internal organs of patients for their diagnoses, since they cannot always have a direct view. So the higher the quality of the images, the better will be the accuracy rates of the diagnoses of the doctors. At present there are several techniques like X-rays, CT scans, and MRIs which are available to help a doctor. However as the number of patients increase, one runs into increasingly larger volumes of data and one of the key challenges here is finding an effective scheme for the economic storage for such huge volumes of data, to be used when processing is needed to be done in the future. It has been seen when that type of data is represented in sparse form then it can be encapsulated into a smaller format, but it does not affect the image quality of the recovered image. In this chapter we present a survey and comparison of the various sparse learning algorithms that can be applied for the efficient and lightweight restructuring of medical images.
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Datta, S., Roy, M. (2021). Sparse Approximation Techniques for Efficient Medical Image Representations. In: Tripathy, H.K., Mishra, S., Mallick, P.K., Panda, A.R. (eds) Technical Advancements of Machine Learning in Healthcare. Studies in Computational Intelligence, vol 936. Springer, Singapore. https://doi.org/10.1007/978-981-33-4698-7_13
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