1 research outputs found
Improving minimum rate predictors algorithm for compression of volumetric medical images
Medical imaging technologies are experiencing a growth in terms of usage and image
resolution, namely in diagnostics systems that require a large set of images, like CT or
MRI. Furthermore, legal restrictions impose that these scans must be archived for several
years. These facts led to the increase of storage costs in medical image databases and
institutions. Thus, a demand for more efficient compression tools, used for archiving and
communication, is arising.
Currently, the DICOM standard, that makes recommendations for medical communications
and imaging compression, recommends lossless encoders such as JPEG, RLE,
JPEG-LS and JPEG2000. However, none of these encoders include inter-slice prediction
in their algorithms.
This dissertation presents the research work on medical image compression, using the
MRP encoder. MRP is one of the most efficient lossless image compression algorithm.
Several processing techniques are proposed to adapt the input medical images to the
encoder characteristics. Two of these techniques, namely changing the alignment of slices
for compression and a pixel-wise difference predictor, increased the compression efficiency
of MRP, by up to 27.9%.
Inter-slice prediction support was also added to MRP, using uni and bi-directional techniques.
Also, the pixel-wise difference predictor was added to the algorithm. Overall, the
compression efficiency of MRP was improved by 46.1%. Thus, these techniques allow for
compression ratio savings of 57.1%, compared to DICOM encoders, and 33.2%, compared
to HEVC RExt Random Access. This makes MRP the most efficient of the encoders
under study