469 research outputs found
More complex encoder is not all you need
U-Net and its variants have been widely used in medical image segmentation.
However, most current U-Net variants confine their improvement strategies to
building more complex encoder, while leaving the decoder unchanged or adopting
a simple symmetric structure. These approaches overlook the true functionality
of the decoder: receiving low-resolution feature maps from the encoder and
restoring feature map resolution and lost information through upsampling. As a
result, the decoder, especially its upsampling component, plays a crucial role
in enhancing segmentation outcomes. However, in 3D medical image segmentation,
the commonly used transposed convolution can result in visual artifacts. This
issue stems from the absence of direct relationship between adjacent pixels in
the output feature map. Furthermore, plain encoder has already possessed
sufficient feature extraction capability because downsampling operation leads
to the gradual expansion of the receptive field, but the loss of information
during downsampling process is unignorable. To address the gap in relevant
research, we extend our focus beyond the encoder and introduce neU-Net (i.e.,
not complex encoder U-Net), which incorporates a novel Sub-pixel Convolution
for upsampling to construct a powerful decoder. Additionally, we introduce
multi-scale wavelet inputs module on the encoder side to provide additional
information. Our model design achieves excellent results, surpassing other
state-of-the-art methods on both the Synapse and ACDC datasets
Hierarchical quantization indexing for wavelet and wavelet packet image coding
In this paper, we introduce the quantization index hierarchy, which is used for efficient coding of quantized wavelet and wavelet packet coefficients. A hierarchical classification map is defined in each wavelet subband, which describes the quantized data through a series of index classes. Going from bottom to the top of the tree, neighboring coefficients are combined to form classes that represent some statistics of the quantization indices of these coefficients. Higher levels of the tree are constructed iteratively by repeating this class assignment to partition the coefficients into larger Subsets. The class assignments are optimized using a rate-distortion cost analysis. The optimized tree is coded hierarchically from top to bottom by coding the class membership information at each level of the tree. Context-adaptive arithmetic coding is used to improve coding efficiency. The developed algorithm produces PSNR results that are better than the state-of-art wavelet-based and wavelet packet-based coders in literature.This research was supported by Isik University BAP-05B302 GrantPublisher's Versio
Perceptual lossless medical image coding
A novel perceptually lossless coder is presented for the compression of medical images. Built on the JPEG 2000 coding framework, the heart of the proposed coder is a visual pruning function, embedded with an advanced human vision model to identify and to remove visually insignificant/irrelevant information. The proposed coder offers the advantages of simplicity and modularity with bit-stream compliance. Current results have shown superior compression ratio gains over that of its information lossless counterparts without any visible distortion. In addition, a case study consisting of 31 medical experts has shown that no perceivable difference of statistical significance exists between the original images and the images compressed by the proposed coder
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