Literature DB >> 18280929

Prediction of perceptible artifacts in JPEG2000 compressed abdomen CT images using a perceptual image quality metric.

Bohyoung Kim1, Kyoung Ho Lee, Kil Joong Kim, Rafal Mantiuk, Vasundhara Bajpai, Tae Jung Kim, Young Hoon Kim, Chang Jin Yoon, Seokyung Hahn.   

Abstract

RATIONALE AND
OBJECTIVES: To test a perceptual quality metric (high-dynamic range visual difference predictor, HDR-VDP) in predicting perceptible artifacts in Joint Photographic Experts Group 2000 compressed thin- and thick-section abdomen computed tomography images.
MATERIALS AND METHODS: A total of 120 thin (0.67 mm) and corresponding thick (5 mm) sections were compressed to ratios from 4:1 to 15:1. Peak signal-to-noise ratio (PSNR), HDR-VDP results (paired t-tests), and five radiologists' pooled responses for the presence of artifacts (exact tests for paired proportions) were compared between the thin and thick sections. For three subsets of 120 thin- (subset A), 120 thick- (subset B), and 60 thin- and 60 thick-section compressed images (subset C), receiver operating curve analysis was performed to compare PSNR and HDR-VDP in predicting the radiologists' responses. Using the cutoff values where the sum of sensitivity and specificity was the maximum in subset C, visually lossless thresholds (VLTs) were estimated for the 240 original images and the estimation accuracy was compared (McNemar test).
RESULTS: Thin sections showed more artifacts in terms of PSNR, HDR-VDP, and radiologists' responses (p < .0001). HDR-VDP outperformed PSNR for subset C (area under the curve: 0.97 versus 0.93, p = 0.03), whereas they did not differ significantly for subset A or B. Using the cutoff values, PSNR and HDR-VDP predicted the VLT accurately for 124 (51.7%) and 183 (76.3%) images, respectively (p < .0001).
CONCLUSIONS: HDR-VDP can predict the perceptible compression artifacts, and therefore can be potentially used to estimate the VLT for such compressions.

Mesh:

Year:  2008        PMID: 18280929     DOI: 10.1016/j.acra.2007.10.018

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  3 in total

1.  Improved pediatric MR imaging with compressed sensing.

Authors:  Shreyas S Vasanawala; Marcus T Alley; Brian A Hargreaves; Richard A Barth; John M Pauly; Michael Lustig
Journal:  Radiology       Date:  2010-06-07       Impact factor: 11.105

2.  vPSNR: a visualization-aware image fidelity metric tailored for diagnostic imaging.

Authors:  Claes Lundström
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-10-16       Impact factor: 2.924

3.  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

  3 in total

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