Perceptually lossless medical image coding

Wu, David M.; Tan, Dunlin; Baird, Marilyn; DeCampo, J.; White, C.; Wu, Hong

doi:10.1109/tmi.2006.870483

Cited by 44 publications

(27 citation statements)

References 37 publications

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“…Recently, lossless compression has been focused on medical applications [29,30] due to increasing resolution of sensing technologies such as 10 T fMRI. In the publishing industry, image quality and printing speed is quickly increasing due to improving mechanical control.…”

Section: Application Example Using Image Datamentioning

confidence: 99%

Lazy Management for Frequency Table on Hardware-Based Stream Lossless Data Compression

et al. 2016

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Abstract:The demand for communicating large amounts of data in real-time has raised new challenges with implementing high-speed communication paths for high definition video and sensory data. It requires the implementation of high speed data paths based on hardware. Implementation difficulties have to be addressed by applying new techniques based on data-oriented algorithms. This paper focuses on a solution for this problem by applying a lossless data compression mechanism on the communication data path. The new lossless data compression mechanism, called LCA-DLT, provides dynamic histogram management for symbol lookup tables used in the compression and the decompression operations. When the histogram memory is fully used, the management algorithm needs to find the least used entries and invalidate these entries. The invalidation operations cause the blocking of the compression and the decompression data stream. This paper proposes novel techniques to eliminate blocking by introducing a dynamic invalidation mechanism, which allows achievement of a high throughput data compression.

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Section: Application Example Using Image Datamentioning

confidence: 99%

Lazy Management for Frequency Table on Hardware-Based Stream Lossless Data Compression

et al. 2016

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“…It is noted that the approach introduced in [47], where a perceptual distortion measure based on a multi-channel vision model is used in place of the MSE in the JPEG2000 coding framework [22], has been extended to coding of medical images, achieving perceptually lossless performance based on statistical analysis of subjective test results, which was participated by 31 radiologists and radiographers [50].…”

Section: Dmentioning

confidence: 99%

Two Fundamental Challenges in Perceptual Picture Coding and Image Restoration

2007

2007 2nd IEEE Conference on Industrial Electronics and Applications

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This paper examines two fundamental challenges in two areas, respectively, which have been intensively researched in the field of image processing and communications, i.e., digital picture coding/ compression and digital picture (including both video and still images) restoration (or de-noising). It reflects on historical developments and reviews the state-of-theart in the area of digital picture coding. Quantitative perceptual distortion measure based on the human visual system is identified as the weakest link in the current picture coding framework and remains a fundamental challenge in devising the next generation picture coding systems. In the area of picture restoration, the paper takes a special interest in surveillance and security video/image de-noising task for forensic investigations. Highlighting the most recent advances and the inadequacies in existing image de-noising techniques, it focuses on a fundamental challenge in designing a unified picture de-noising framework, including noise modeling, for removal of analogue and digital surveillance video distortions. 1

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“…Singh and Sharma (2012) have presented a hybrid model which is the combination of several compression techniques such as DWT and DCT, followed by Huffman encoding technique. Wu et al (2006) have proposed a coder which outperforms the LOCO coder while preserving the visual fidelity of the image the heart. Sivanantha Raja et al (2012) have described a novel approach to medical image compression using the curvelet transform.…”

Section: Introductionmentioning

confidence: 99%

Region-based medical image compression in teleradiology

Moorthi

Amutha²

2015

IJTMCP

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Digital radiology has resulted in significant increase in use of digital medical images in the process of diagnosis. Lossless image compression is necessary to preserve the value of diagnostic medical images. This method will be applicable to mainly compressing information without any loss. More particularly, this paper aims to increase compression ratio and quality (PSNR) of medical images. Initially, MRI brain image is segmented using grow cut. This algorithm extracts the region of tumour (abnormal region) and non-region of tumour (normal region) parts. The region of tumour is selected using seed point which is selected by extracting run length features. Morphological processing is used for highlighting the tumour region. The integer wavelet transform is applied on both region of tumour and non-region of tumour parts. The abnormal region is compressed losslessly using arithmetic coding and normal region is compressed using lossy compression (i.e.) EZW coding. Finally, the compressed image of the region of tumour (abnormal region) is fused with compressed image of the normal region. This implementation is found to possess superior reconstruction properties and better compression ratio.

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Perceptually lossless medical image coding

Cited by 44 publications

References 37 publications

Lazy Management for Frequency Table on Hardware-Based Stream Lossless Data Compression

Lazy Management for Frequency Table on Hardware-Based Stream Lossless Data Compression

Two Fundamental Challenges in Perceptual Picture Coding and Image Restoration

Region-based medical image compression in teleradiology

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