title: Fractal-feature distance analysis of contrast-detail phantom image and meaning of pseudo fractal dimension and complexity.
abstract: The purposes of our studies are to examine whether or not fractal-feature distance deduced from virtual volume method can simulate observer performance indices and to investigate the physical meaning of pseudo fractal dimension and complexity. Contrast-detail (C-D) phantom radiographs were obtained at various mAs values (0.5 - 4.0 mAs) and 140 kVp with a computed radiography system, and the reference image was acquired at 13 mAs. For all C-D images, fractal analysis was conducted using the virtual volume method that was devised with a fractional Brownian motion model. The fractal-feature distances between the considered and reference images were calculated using pseudo fractal dimension and complexity. Further, we have performed the C-D analysis in which ten radiologists participated, and compared the fractal-feature distances with the image quality figures (IQF). To clarify the physical meaning of the pseudo fractal dimension and complexity, contrast-to-noise ratio (CNR) and standard deviation (SD) of images noise were calculated for each mAs and compared with the pseudo fractal dimension and complexity, respectively. A strong linear correlation was found between the fractal-feature distance and IQF. The pseudo fractal dimensions became large as CNR increased. Further, a linear correlation was found between the exponential complexity and image noise SD.
authors.affiliation: Department of Radiological Technology, Nagoya University School of Health Sciences, Nagoya, Japan. imai@met.nagoya-u.ac.jp
authors.forename: K
authors.initials: K
authors.lastname: Imai
authors.forename: M
authors.initials: M
authors.lastname: Ikeda
authors.forename: Y
authors.initials: Y
authors.lastname: Enchi
authors.forename: T
authors.initials: T
authors.lastname: Niimi
mesh_terms: D000465:Algorithms; D017709:Fractals; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000295:instrumentation / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*