Lossless Compression of Medical Images Using 3-D Predictors

Lucas, Luı́s; Rodrigues, Nuno M. M.; Cruz, Luís A. da Silva; Faria, Sérgio M. M. de

doi:10.1109/tmi.2017.2714640

Cited by 75 publications

(55 citation statements)

References 18 publications

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“…Santos et al [31] processed each frame sequentially and using 3D predictors based on the previously encoded frames. Lucas et al [20] further adopted 3D block classification to process the data at the volume level.…”

Section: D Medical Image Compressionmentioning

confidence: 99%

Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds

Liu

Zhuang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Cloud based medical image analysis has become popular recently due to the high computation complexities of various deep neural network (DNN) based frameworks and the increasingly large volume of medical images that need to be processed. It has been demonstrated that for medical images the transmission from local to clouds is much more expensive than the computation in the clouds itself. Towards this, 3D image compression techniques have been widely applied to reduce the data traffic. However, most of the existing image compression techniques are developed around human vision, i.e., they are designed to minimize distortions that can be perceived by human eyes. In this paper we will use deep learning based medical image segmentation as a vehicle and demonstrate that interestingly, machine and human view the compression quality differently. Medical images compressed with good quality w.r.t. human vision may result in inferior segmentation accuracy. We then design a machine vision oriented 3D image compression framework tailored for segmentation using DNNs. Our method automatically extracts and retains image features that are most important to the segmentation. Comprehensive experiments on widely adopted segmentation frameworks with HVSMR 2016 challenge dataset show that our method can achieve significantly higher segmentation accuracy at the same compression rate, or much better compression rate under the same segmentation accuracy, when compared with the existing JPEG 2000 method. To the best of the authors' knowledge, this is the first machine vision guided medical image compression framework for segmentation in the clouds.

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Section: D Medical Image Compressionmentioning

confidence: 99%

Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds

Liu

Zhuang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Compression performances of different image compression standards have been extensively studied on medical images in many studies in the existing literature [73,[77][78][79][80][106][107][108][109][110][111][112][113]. Notwithstanding, to the best of our knowledge, compression performance results in the literature are not simultaneously available for all image modalities described in Section 2 for lossless and lossy compression using the standards described in Section 3 with comparable parameters.…”

Section: Compression Performance Of Image Compression Standards On Mementioning

confidence: 99%

The Current Role of Image Compression Standards in Medical Imaging

et al. 2017

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Abstract:With the increasing utilization of medical imaging in clinical practice and the growing dimensions of data volumes generated by various medical imaging modalities, the distribution, storage, and management of digital medical image data sets requires data compression. Over the past few decades, several image compression standards have been proposed by international standardization organizations. This paper discusses the current status of these image compression standards in medical imaging applications together with some of the legal and regulatory issues surrounding the use of compression in medical settings.

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“…Lossless image compression has wide applications such as the archival of medical [201][202][203] or business documents and digital radiography where any loss of information in the original image can result in improper diagnosis [2]. Other applications of lossless compression include the compression of image for camera system [200], the storage and transmission of thermal images captured by Nano-satellite [204] and remote sensing applications such as monitoring forest fires and determining the soil moisture [205].…”

Section: Introductionmentioning

confidence: 99%

Image compression techniques: A survey in lossless and lossy algorithms

2018

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The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System.

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Lossless Compression of Medical Images Using 3-D Predictors

Cited by 75 publications

References 18 publications

Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds

Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds

The Current Role of Image Compression Standards in Medical Imaging

Image compression techniques: A survey in lossless and lossy algorithms

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