Wavelet based volumetric medical image compression

Bruylants, Tim; Munteanu, Adrian; Schelkens, Peter

doi:10.1016/j.image.2014.12.007

Cited by 115 publications

(68 citation statements)

References 41 publications

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“…In [21], Zhang et al (2008) have presented an image compression method, called prediction by partial approximate matching (PPAM) method which involves four basic steps, namely, preprocessing, prediction, context modelling, and arithmetic coding. In [22], B.T.A Munteanu, and Peter Schelkens (2015) have proposed a lossy compression scheme for medical images which makes use of a generic codec framework that supports JPEG 2000 with its volumetric extension (JP3D), and directional wavelet transforms.…”

Section: Related Workmentioning

confidence: 99%

Lossy image compression based on prediction error and vector quantisation

Ayoobkhan

Eswaran

Ramakrishnan

2017

J Image Video Proc.

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Lossy image compression has been gaining importance in recent years due to the enormous increase in the volume of image data employed for Internet and other applications. In a lossy compression, it is essential to ensure that the compression process does not affect the quality of the image adversely. The performance of a lossy compression algorithm is evaluated based on two conflicting parameters, namely, compression ratio and image quality which is usually measured by PSNR values. In this paper, a new lossy compression method denoted as PE-VQ method is proposed which employs prediction error and vector quantization (VQ) concepts. An optimum codebook is generated by using a combination of two algorithms, namely, artificial bee colony and genetic algorithms. The performance of the proposed PE-VQ method is evaluated in terms of compression ratio (CR) and PSNR values using three different types of databases, namely, CLEF med 2009, Corel 1 k and standard images (Lena, Barbara etc.). Experiments are conducted for different codebook sizes and for different CR values. The results show that for a given CR, the proposed PE-VQ technique yields higher PSNR value compared to the existing algorithms. It is also shown that higher PSNR values can be obtained by applying VQ on prediction errors rather than on the original image pixels.

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Section: Related Workmentioning

confidence: 99%

Lossy image compression based on prediction error and vector quantisation

Ayoobkhan

Eswaran

Ramakrishnan

2017

J Image Video Proc.

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“…13 For that reason, we show how the JPEG 2000 architecture can easily be extended to include the block-based directional adaptive DWT (DA-DWT). [32][33][34] We employ a separable lifting scheme, similar to that of JPEG 2000 DWT, but with modified prediction and update functions that are no longer confined only to the horizontal direction (1,0) for row-based splits and the vertical direction (0,1) for column-based splits. Doing so enables the directional DWT to adapt to local geometric features by adjusting its operational direction.…”

Section: Directional Adaptive Discrete Wavelet Transformmentioning

confidence: 99%

JPEG 2000-based compression of fringe patterns for digital holographic microscopy

et al. 2014

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Abstract. With the advent of modern computing and imaging technologies, digital holography is becoming widespread in various scientific disciplines such as microscopy, interferometry, surface shape measurements, vibration analysis, data encoding, and certification. Therefore, designing an efficient data representation technology is of particular importance. Off-axis holograms have very different signal properties with respect to regular imagery, because they represent a recorded interference pattern with its energy biased toward the high-frequency bands. This causes traditional images' coders, which assume an underlying 1∕f 2 power spectral density distribution, to perform suboptimally for this type of imagery. We propose a JPEG 2000-based codec framework that provides a generic architecture suitable for the compression of many types of off-axis holograms. This framework has a JPEG 2000 codec at its core, extended with (1) fully arbitrary wavelet decomposition styles and (2) directional wavelet transforms. Using this codec, we report significant improvements in coding performance for off-axis holography relative to the conventional JPEG 2000 standard, with Bjøntegaard delta-peak signal-to-noise ratio improvements ranging from 1.3 to 11.6 dB for lossy compression in the 0.125 to 2.00 bpp range and bit-rate reductions of up to 1.6 bpp for lossless compression.

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“…Most of these standards use frequency transformation to filter out information that leads to little visual distortion. In addition to the existing 3D image compression standards, alternative compression methods have been proposed in the literature, most of which modify the existing standards to improve their performance [5] [30][29] [49]. There are also a few methods for lossless compression of 3D medical images [31] [20].…”

Section: Introductionmentioning

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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Wavelet based volumetric medical image compression

Cited by 115 publications

References 41 publications

Lossy image compression based on prediction error and vector quantisation

Lossy image compression based on prediction error and vector quantisation

JPEG 2000-based compression of fringe patterns for digital holographic microscopy

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

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