Literature DB >> 9704285

Wavelet versus JPEG (Joint Photographic Expert Group) and fractal compression. Impact on the detection of low-contrast details in computed radiographs.

J Ricke1, P Maass, E Lopez Hänninen, T Liebig, H Amthauer, C Stroszczynski, W Schauer, T Boskamp, M Wolf.   

Abstract

RATIONALE AND
OBJECTIVES: The aim of this study was to evaluate different lossy image compression algorithms in direct comparison.
METHODS: Computed radiographs were reviewed after compression with Wavelet, Fractal, and Joint Photographic Expert Group (JPEG) algorithms. For receiver operating characteristic (ROC) analysis, 54 thoracic computed radiographs (31 showing pulmonary nodules) were compressed with a ratio of 1:60. Five images of a test-phantom were coded at 1:13. All images were reviewed on a PC. Uncompressed images were reviewed at a PC and at a radiologic workstation (with image processing).
RESULTS: For thorax images, decrease of diagnostic accuracy was significant with Wavelets. Fractal performed worse than Wavelets. No ROC curve was observed for JPEG due to poor image quality. No diagnostic loss was noted comparing PC and Workstation review. For low-contrast details of the phantom, results of Wavelet compression were equal to uncompressed images. Fewer true positives and increased true negatives were noted with Wavelets though. Wavelets were superior to JPEG, and JPEG images were superior to Fractal. Workstation review was superior to PC review.
CONCLUSIONS: Only Wavelets provided accurate review of low-contrast details at a compression of 1:13. Frequency filtering of Wavelets affects contrast even at a low compression ratio. JPEG performed better than Fractal at low and worse at high compression ratio.

Mesh:

Year:  1998        PMID: 9704285     DOI: 10.1097/00004424-199808000-00006

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  9 in total

1.  Diagnostic accuracy of film-based, TIFF, and wavelet compressed digital temporomandibular joint images.

Authors:  C J Trapnell; W C Scarfe; J H Cook; A M Silvejra; F J Regennitter; B S Haskell
Journal:  J Digit Imaging       Date:  2000-02       Impact factor: 4.056

2.  Wavelet compression on detection of brain lesions with magnetic resonance imaging.

Authors:  S Terae; K Miyasaka; K Kudoh; T Nambu; T Shimizu; K Kaneko; H Yoshikawa; R Kishimoto; T Omatsu; N Fujita
Journal:  J Digit Imaging       Date:  2000-11       Impact factor: 4.056

3.  Effect of digital image compression on screening for diabetic retinopathy.

Authors:  R S Newsom; A Clover; M T Costen; J Sadler; J Newton; A J Luff; C R Canning
Journal:  Br J Ophthalmol       Date:  2001-07       Impact factor: 4.638

4.  A comparison of wavelet and Joint Photographic Experts Group lossy compression methods applied to medical images.

Authors:  T A Iyriboz; M J Zukoski; K D Hopper; P L Stagg
Journal:  J Digit Imaging       Date:  1999-05       Impact factor: 4.056

5.  Visually lossless threshold determination for microcalcification detection in wavelet compressed mammograms.

Authors:  O Kocsis; L Costaridou; L Varaki; E Likaki; C Kalogeropoulou; S Skiadopoulos; G Panayiotakis
Journal:  Eur Radiol       Date:  2003-02-15       Impact factor: 5.315

6.  JPEG2000 for automated quantification of immunohistochemically stained cell nuclei: a comparative study with standard JPEG format.

Authors:  Marylène Lejeune; Carlos López; Ramón Bosch; Anna Korzyńska; Maria-Teresa Salvadó; Marcial García-Rojo; Urszula Neuman; Łukasz Witkowski; Jordi Baucells; Joaquín Jaén
Journal:  Virchows Arch       Date:  2010-11-18       Impact factor: 4.064

7.  Quality of compressed medical images.

Authors:  Ya-Hui Shiao; Tzong-Jer Chen; Keh-Shih Chuang; Cheng-Hsun Lin; Chun-Chao Chuang
Journal:  J Digit Imaging       Date:  2007-02-22       Impact factor: 4.056

8.  JPEG2000 still image coding quality.

Authors:  Tzong-Jer Chen; Sheng-Chieh Lin; You-Chen Lin; Ren-Gui Cheng; Li-Hui Lin; Wei Wu
Journal:  J Digit Imaging       Date:  2013-10       Impact factor: 4.056

9.  Automated Bayesian model development for frequency detection in biological time series.

Authors:  Emma Granqvist; Giles E D Oldroyd; Richard J Morris
Journal:  BMC Syst Biol       Date:  2011-06-24
  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.