title: Evaluation of compressed lung CT image quality using quantitative analysis.
abstract: The goals of this study were (1) to evaluate the quality of compressed lung CT images obtained using high resolution CT (HRCT: 2 mm slice thickness) for degree of compression and conventional CT (10 mm slice thickness) images by using physical and subjective evaluations, and (2) to analyze the distortion of density distribution on lung CT images using histogram analysis for each compression ratio. The coding method was performed according to the Joint Photographic Experts Group (JPEG). We physically evaluated the quality of compressed lung CT images using the peak signal-to-noise ratio (PSNR) as given by the square root of the ratio of the peak value of the gray level squared to the mean square error (dB) and subjectively evaluated the CT images using the mean opinion score (MOS). The acceptable compression ratio for diagnosis was about 1:6 to 1:7 for conventional CT images and about 1:4 to 1:5 for HRCT images as determined by MOS. The PSNR corresponding to acceptable compression ratios was about 50 dB. The difference in density distribution between HRCT and conventional CT was statistically significant (Friedman test: p<0.02) in histogram analysis. Results suggested that, in comparison with conventional CT, a high compression ratio was not suitable for HRCT.
authors.affiliation: Department of Radiology, Osaka University Hospital, Suita, Japan.
authors.forename: S
authors.initials: S
authors.lastname: Yamamoto
authors.forename: T
authors.initials: T
authors.lastname: Johkoh
authors.forename: N
authors.initials: N
authors.lastname: Mihara
authors.forename: T
authors.initials: T
authors.lastname: Umeda
authors.forename: M
authors.initials: M
authors.lastname: Azuma
authors.forename: S
authors.initials: S
authors.lastname: Nakanishi
authors.forename: Y
authors.initials: Y
authors.lastname: Narumi
authors.forename: H
authors.initials: H
authors.lastname: Naito
authors.forename: H
authors.initials: H
authors.lastname: Nakamura
mesh_terms: D000465:Algorithms; D006801:Humans; D008171:Lung Diseases / Q000175:diagnosis*; D011856:Radiographic Image Enhancement; D012815:Signal Processing, Computer-Assisted; D014057:Tomography, X-Ray Computed