Hierarchical Oriented Predictions for Resolution Scalable Lossless and Near-Lossless Compression of CT and MRI Biomedical Images

Taquet, Jonathan; Labit, Claude

doi:10.1109/tip.2012.2186147

Cited by 39 publications

(15 citation statements)

References 18 publications

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“…Taquet et al [27] have highlighted a hierarchical approach to resolution scalable lossless and near-lossless (NLS) compression that integrates the adaptability of DPCM schemes with new hierarchical oriented predictors to provide resolution scalability with better compression performances than the usual hierarchical interpolation predictor or the wavelet transform. A prediction level of the HOP scheme is performed in two steps.…”

Section: Hierarchical Oriented Predictions Approachmentioning

confidence: 99%

A Review of the Effective Techniques of Compression in Medical Image Processing

Suma¹,

Sridhar²

2014

IJCA

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Medical image data (Ultrasonography, Computed Tomography, Magnetic Resonance Imaging etc.) consumes maximum storage and utilize maximum bandwidth for transmission that often results in degradation of image quality. Due to these inherent issues in such type of images, compression is the only applicable technique explored in the due course of prior research work. Currently, there exists abundant research work on medical image compression considering lossy and lossless types, but the need of medical images to be compressed efficiently with optimal compression ratio is yet a question mark. This paper will perform an investigation of various techniques explored and discusses some of the efficient techniques explicitly among all the prior work. While reviewing the prior literatures, it was explored that although medical image compression is an emerging need, but it encounters higher dimensionality of challenges and complicatedness for catering the increasing demands of the medical science.

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Section: Hierarchical Oriented Predictions Approachmentioning

confidence: 99%

A Review of the Effective Techniques of Compression in Medical Image Processing

Suma¹,

Sridhar²

2014

IJCA

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“…Context based adaptive lossless image codec (CALIC) obtains higher lossless compression ratio of continuous tone images [6] that are frequently used as references in [7]. In lossless compression, resolution or rate scalability coding is allowed by HIP approaches such as hierarchical interpolation (HINT) [8] and derivative works such as interleaved HINT (IHINT) [8]. For color image compression, the RGB the components are initially decomposed by the transform and each of the RGB component is compressed individually by one of the above reference models.…”

Section: Introductionmentioning

confidence: 99%

Lossless compression based on hierarchical extrapolation for biomedical imaging applications

Vallathan¹,

Jayanthi²

2015

2015 International Conference on Microwave, Optical and Communication Engineering (ICMOCE)

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The imaging systems like CT, MRI scan exhibits huge amount of digital data and therefore compression becomes crucial for storage and communication resolves. Most of the recent compression schemes offers a very high compression ratio with significant loss of image quality and do not always perform better for all sets of similar images. This work aims at resolving this issue, which serves as the motivation. This paper presents a lossless image compression based on hierarchical extrapolation for medical images using Haar transform and through Embedded Encoding technique. The compression technique proves to be lossless and as well perform better for a variety of images including CT scan, MRI and ultrasound biomedical images than the existing schemes. The performance metrics namely Peak Signal to Noise Ratio, Compression ratio and mean square error values are computed for the compressed image for evaluation . The performance metrics attained through the proposed algorithm is bench marked with JPEG 2000. The result section of this paper brings forth the relative improvement offered by the proposed logic.

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“…The former is the variance of the errors essentially equivalent to the peak signal to noise ratio (PSNR). The latter is the maximum of the absolute value of the errors, well utilized in the near lossless coding [6][7][8][9][10]. We consider both of them to design a new range reduction method as inspired by the recent paper [11].…”

Section: Introductionmentioning

confidence: 99%

Range reduction of HDR images for backward compatibility with LDR image processing

Iwahashi

Yoshida

Kiya

2014

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific

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This paper proposes a new range reduction method with the minimum amount of quantization error in the L2 norm under the L infinity norm constraint. It is necessary to reduce dynamic range of pixel values of high dynamic range (HDR) images to have backward compatibility with low dynamic range image processing systems. The simplest approach is to truncate lower bit planes in binary representation of pixel values. However it does not have fine granularity of the reduced range, and also it does not utilize the histogram sparseness. Furthermore, it generates significant amount of quantization errors. In this paper, we propose a new range reduction method which can 1) utilize the histogram sparseness, and also 2) minimize variance of the error 3) under a specified maximum absolute value of the error.

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Hierarchical Oriented Predictions for Resolution Scalable Lossless and Near-Lossless Compression of CT and MRI Biomedical Images

Cited by 39 publications

References 18 publications

A Review of the Effective Techniques of Compression in Medical Image Processing

A Review of the Effective Techniques of Compression in Medical Image Processing

Lossless compression based on hierarchical extrapolation for biomedical imaging applications

Range reduction of HDR images for backward compatibility with LDR image processing

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