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Lossless Compression of Segmented CT Medical Images According to the Hounsfield Scale

Authors: Denis Spelic; Domen Mongus; Borut Zalik;

Lossless Compression of Segmented CT Medical Images According to the Hounsfield Scale

Abstract

In this paper, a method for loss less compression of medical CT images is presented. The method allows separate compression, transmission, and decompression using data segmentation based on the Hounsfield scale. The presented method modifies our previous method by modifying the prediction scheme. The prediction scheme omits the inter-slice prediction to allow random access into the compressed segmented CT slides. The results show that the obtained compression rate is comparable to previous method.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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