Literature DB >> 16964859

The impact of image information on compressibility and degradation in medical image compression.

Ales Fidler1, Uros Skaleric, Bostjan Likar.   

Abstract

The aim of the study was to demonstrate and critically discuss the influence of image information on compressibility and image degradation. The influence of image information on image compression was demonstrated on the axial computed tomography images of a head. The standard Joint Photographic Expert Group (JPEG) and JPEG 2000 compression methods were used in compression ratio (CR) and in quality factor (QF) compression modes. Image information was estimated by calculating image entropy, while the effects of image compression were evaluated quantitatively, by file size reduction and by local and global mean square error (MSE), and qualitatively, by visual perception of distortion in high and low contrast test patterns. In QF compression mode, a strong correlation between image entropy and file size was found for JPEG (r=0.87, p < 0.001) and JPEG 2000 (r=0.84, p < 0.001), while corresponding local MSE was constant (4.54) or nearly constant (2.36-2.37), respectively. For JPEG 2000 CR compression mode, CR was nearly constant (1:25), while local MSE varied considerably (2.26 and 10.09). The obtained qualitative and quantitative results clearly demonstrate that image degradation highly depends on image information, which indicates that the degree of image degradation cannot be guaranteed in CR but only in QF compression mode. CR is therefore not a measure of choice for expressing the degree of image degradation in medical image compression. Moreover, even when using QF compression modes, objective evaluation, and comparison of the compression methods within and between studies is often not possible due to the lack of standardization of compression quality scales.

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Year:  2006        PMID: 16964859     DOI: 10.1118/1.2218316

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  8 in total

1.  Computed Tomography Image Compressibility and Limitations of Compression Ratio-Based Guidelines.

Authors:  Jean-François Pambrun; Rita Noumeir
Journal:  J Digit Imaging       Date:  2015-12       Impact factor: 4.056

2.  Four-Dimensional Cone-Beam Computed Tomography Image Compression Using Video Encoder for Radiotherapy.

Authors:  Hui Yan; Yexiong Li; Jianrong Dai
Journal:  J Digit Imaging       Date:  2020-10       Impact factor: 4.056

3.  Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction.

Authors:  Xiaoying Song; Qijun Huang; Sheng Chang; Jin He; Hao Wang
Journal:  J Digit Imaging       Date:  2016-12       Impact factor: 4.056

4.  A Block Adaptive Near-Lossless Compression Algorithm for Medical Image Sequences and Diagnostic Quality Assessment.

Authors:  Urvashi Sharma; Meenakshi Sood; Emjee Puthooran
Journal:  J Digit Imaging       Date:  2020-04       Impact factor: 4.056

5.  Exploring correlation information for image compression of four-dimensional computed tomography.

Authors:  Hui Yan; Yexiong Li; Jianrong Dai
Journal:  Quant Imaging Med Surg       Date:  2019-07

6.  Determining optimal medical image compression: psychometric and image distortion analysis.

Authors:  Alexander C Flint
Journal:  BMC Med Imaging       Date:  2012-07-31       Impact factor: 1.930

7.  Development of an algorithm to automatically compress a CT image to visually lossless threshold.

Authors:  Chang-Mo Nam; Kyong Joon Lee; Yousun Ko; Kil Joong Kim; Bohyoung Kim; Kyoung Ho Lee
Journal:  BMC Med Imaging       Date:  2018-12-17       Impact factor: 1.930

8.  Evaluation of video compression methods for cone-beam computerized tomography.

Authors:  Hui Yan; Yexiong Li; Jianrong Dai
Journal:  J Appl Clin Med Phys       Date:  2019-05-09       Impact factor: 2.102

  8 in total

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