It is not acceptable to use lossless techniques when compressing medical image data, and as a result, it is difficult to achieve high compression ratios. We have developed a novel segmentation based compression scheme...
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ISBN:
(纸本)9781467302302
It is not acceptable to use lossless techniques when compressing medical image data, and as a result, it is difficult to achieve high compression ratios. We have developed a novel segmentation based compression scheme to overcome this problem and our experimental results indicate that this scheme is capable of achieving a high level of compression without sacrificing the quality of the patient data.
Improvements in medicine and healthcare are accelerating. Information generation, sharing, and expert analysis, play a great role in improving medical sciences. Big data produced by medical procedures in hospitals, la...
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Improvements in medicine and healthcare are accelerating. Information generation, sharing, and expert analysis, play a great role in improving medical sciences. Big data produced by medical procedures in hospitals, laboratories, and research centers needs storage and transmission. datacompression is a critical tool that reduces the burden of storage and transmission. medical images, in particular, require special consideration in terms of storage and transmissions. Unlike many other types of big data, medical images require lossless storage. Special purpose compression algorithms and codecs could compress variety of such images with superior performance compared to the general purpose lossless algorithms. For the medical images, many lossless algorithms have been proposed so far. A compression algorithm comprises of different stages. Before designing a special purpose compression method we need to know how much each stage contributes to the overall compression performance so we could accordingly invest time and effort in designing different stages. In order to compare and evaluate these multi stage compression techniques and to design more efficient compression methods for big data applications, in this paper the effectiveness of each of these compression stages on the total performance of the algorithm is analyzed. (C) 2016 Elsevier Ltd. All rights reserved.
Due to the substantial storage requirements of the 4D medical images, achieving efficient compression of such images is a crucial topic. Existing traditional image/video coding methods have achieved remarkable results...
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ISBN:
(纸本)9798331529543;9798331529550
Due to the substantial storage requirements of the 4D medical images, achieving efficient compression of such images is a crucial topic. Existing traditional image/video coding methods have achieved remarkable results in most compression tasks, but their performance in encoding 4D medical images remain poor. This is because these methods cannot fully exploit the spatio-temporal correlations in 4D images. Recently, implicit neural representation (INR) based image/video compression methods have made significant progress, with coding performance comparable to traditional methods. However, they also suffer from significant performance losses in 4D medical image compression like traditional methods. In this paper, we propose an efficient hybrid representation framework, which includes six learnable feature planes and a tiny MLP decoder. This framework alleviates the issue of previous methods lacking the ability to utilize the spatio-temporal correlations in 4D medical images, enabling it to capture these information more effectively. We also introduce a novel adaptive plane scaling strategy that allocates the numbers of parameter in each plane based on the resolution of the image. This design allows the model to further enhance the reconstruction quality at the same compression ratio. Extensive experiments show that our model achieves better RD performance compared to traditional and INR-based methods, and it also offers faster encoding speeds than INR-based methods.
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