Literature DB >> 11110257

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

S Terae1, K Miyasaka, K Kudoh, T Nambu, T Shimizu, K Kaneko, H Yoshikawa, R Kishimoto, T Omatsu, N Fujita.   

Abstract

The purpose of this report is to assess clinically acceptable compression ratios on the detection of brain lesions at magnetic resonance imaging (MRI). Four consecutive T2-weighted and the corresponding T1-weighted images obtained in 20 patients were studied for 109 anatomic sites including 50 with lesions and 59 without lesions. The images were obtained on a 1.5-T MR unit with a pixel size of 0.9 to 1.2 x 0.47 mm and a section thickness of 5 mm. The image data were compressed by wavelet-based algorithm at ratios of 20:1, 40:1, and 60:1. Three radiologists reviewed these images on an interactive workstation and rated the presence or absence of a lesion with a 50 point scale for each anatomic site. The authors also evaluated the influence of pixel size on the quality of image compression. At receiver operating characteristic (ROC) analysis, no statistically significant difference was detected at a compression ratio of 20:1. A significant difference was observed with 40:1 compressed images for one reader (P = .023), and with 60:1 for all readers (P = .001 to .012). A root mean squared error (RMSE) was higher in 0.94- x 0.94-mm pixel size images than in 0.94- x 0.47-mm pixel size images at any compression ratio, indicating compression tolerance is lower for the larger pixel size images. The RMSE, subjective image quality, and error images of 10:1 compressed 0.94- x 0.94-mm pixel size images were comparable with those of 20:1 compressed 0.94- x 0.47-mm pixel size images. Wavelet compression can be acceptable clinically at ratios as high as 20:1 for brain MR images when a pixel size at image acquisition is around 1.0 x 0.5 mm, and as high as 10:1 for those with a pixel size around 1.0 x 1.0 mm.

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Year:  2000        PMID: 11110257      PMCID: PMC3453072          DOI: 10.1007/bf03168393

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  18 in total

1.  Best parameters selection for wavelet packet-based compression of magnetic resonance images.

Authors:  A N Abu-Rezq; A S Tolba; G A Khuwaja; S G Foda
Journal:  Comput Biomed Res       Date:  1999-10

2.  Performance analysis of a new semiorthogonal spline wavelet compression algorithm for tonal medical images.

Authors:  S K Thompson; J D Hazle; D F Schomer; A A Elekes; D A Johnston; J Huffman; C K Chui
Journal:  Med Phys       Date:  2000-02       Impact factor: 4.071

3.  Clinical evaluation of irreversible data compression for computed radiography of the hand.

Authors:  K Uchida; H Watanabe; T Aoki; K Nakamura; H Nakata
Journal:  J Digit Imaging       Date:  1998-08       Impact factor: 4.056

4.  Statistical comparison of two ROC-curve estimates obtained from partially-paired datasets.

Authors:  C E Metz; B A Herman; C A Roe
Journal:  Med Decis Making       Date:  1998 Jan-Mar       Impact factor: 2.583

5.  Impact of lossy image compression on accuracy of caries detection in digital images taken with a storage phosphor system.

Authors:  A Wenzel; E Gotfredsen; E Borg; H G Gröndahl
Journal:  Oral Surg Oral Med Oral Pathol Oral Radiol Endod       Date:  1996-03

6.  An analytical look at the effects of compression on medical images.

Authors:  K Persons; P Palisson; A Manduca; B J Erickson; V Savcenko
Journal:  J Digit Imaging       Date:  1997-08       Impact factor: 4.056

7.  Evaluation of irreversible compression of digitized posterior-anterior chest radiographs.

Authors:  B J Erickson; A Manduca; K R Persons; F Earnest; T E Hartman; G F Harms; L R Brown
Journal:  J Digit Imaging       Date:  1997-08       Impact factor: 4.056

8.  Displaying radiologic images on personal computers: image storage and compression: Part 1.

Authors:  T Gillespy; A H Rowberg
Journal:  J Digit Imaging       Date:  1993-11       Impact factor: 4.056

9.  Focal hepatic lesions: effect of three-dimensional wavelet compression on detection at CT.

Authors:  M A Goldberg; G S Gazelle; G W Boland; P F Hahn; W W Mayo-Smith; M Pivovarov; E F Halpern; J Wittenberg
Journal:  Radiology       Date:  1997-01       Impact factor: 11.105

10.  Effect of CT digital image compression on detection of coronary artery calcification.

Authors:  L M Zheng; S Sone; Y Itani; Q Wang; K Hanamura; K Asakura; F Li; Z G Yang; J C Wang; T Funasaka
Journal:  Acta Radiol       Date:  2000-03       Impact factor: 1.701

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  2 in total

Review 1.  Compressed sensing MRI: a review of the clinical literature.

Authors:  Oren N Jaspan; Roman Fleysher; Michael L Lipton
Journal:  Br J Radiol       Date:  2015-09-24       Impact factor: 3.039

2.  Quantitative image quality evaluation of MR images using perceptual difference models.

Authors:  Jun Miao; Donglai Huo; David L Wilson
Journal:  Med Phys       Date:  2008-06       Impact factor: 4.071

  2 in total

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