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Publication
JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS
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Authors
Abstract(s)
The massive volume of data generated daily by the gathering of medical images with
different modalities might be difficult to store in medical facilities and share through
communication networks. To alleviate this issue, efficient compression methods
must be implemented to reduce the amount of storage and transmission resources
required in such applications. However, since the preservation of all image details
is highly important in the medical context, the use of lossless image compression
algorithms is of utmost importance.
This thesis presents the research results on a lossless compression scheme designed
to encode both computerized tomography (CT) and positron emission tomography
(PET). Different techniques, such as image-to-image translation, intra prediction,
and inter prediction are used. Redundancies between both image modalities are
also investigated. To perform the image-to-image translation approach, we resort to
lossless compression of the original CT data and apply a cross-modality image translation
generative adversarial network to obtain an estimation of the corresponding
PET.
Two approaches were implemented and evaluated to determine a PET residue
that will be compressed along with the original CT. In the first method, the
residue resulting from the differences between the original PET and its estimation
is encoded, whereas in the second method, the residue is obtained using encoders
inter-prediction coding tools. Thus, in alternative to compressing two independent
picture modalities, i.e., both images of the original PET-CT pair solely the CT is
independently encoded alongside with the PET residue, in the proposed method.
Along with the proposed pipeline, a post-processing optimization algorithm that
modifies the estimated PET image by altering the contrast and rescaling the image
is implemented to maximize the compression efficiency.
Four different versions (subsets) of a publicly available PET-CT pair dataset
were tested. The first proposed subset was used to demonstrate that the concept
developed in this work is capable of surpassing the traditional compression schemes.
The obtained results showed gains of up to 8.9% using the HEVC. On the other
side, JPEG2k proved not to be the most suitable as it failed to obtain good results,
having reached only -9.1% compression gain. For the remaining (more challenging) subsets, the results reveal that the proposed refined post-processing scheme attains,
when compared to conventional compression methods, up 6.33% compression gain
using HEVC, and 7.78% using VVC.
Description
Keywords
Computerized tomography Positron emission tomography Lossless compression Generative Adversarial Network Image-to-image translation Medical Image Coding