Fractal-feature distance as a substitute for observer performance index in contrast-detail examination.
OBJECTIVE
To assess whether or not the fractal-feature distance using the box-counting algorithm can be a substitute for observer performance index.


METHODS
Contrast-detail (C-D) phantom images were obtained at various mAs-values (0.5-4.0 mAs) and 140 kV(p) with a Fuji computed radiography system, and the reference image was acquired at 50 mAs; all cylindrical targets in the C-D phantom were visualized on this image. The C-D images were converted to binary images using the profile curves around the smallest cylindrical target images on the reference images. The fractal analysis was conducted using the box-counting algorithm for these binary images. The fractal-feature distances between the low-dose and reference images were calculated using the fractal dimension and the complexity. Furthermore, we performed the C-D analysis in which ten radiologists participated, and compared the fractal-feature distances with the image quality figures (IQF) derived from the C-D analysis with Markov chain.


RESULTS
For all C-D phantom radiographs, the relationship between the length of the square boxes and the number of boxes to cover the positive pixels of the binary image was linear on a log-log scale (r>or=0.999). A strong linear correlation was found between the fractal-feature distance and IQF (r=0.990).


CONCLUSIONS
We have shown that the binary image of C-D phantom can be analyzed by the box-counting algorithm and its fractal-feature distance increases as the radiation dose decreases. Furthermore, we have shown that the fractal-feature distances will be equivalent to IQFs in C-D analysis.
Imai
Kuniharu
K
Department of Radiological Technology, Nagoya University School of Health Sciences, 1-20 Daikominami 1-chome, Higashi-ku, Nagoya 461-8673, Japan. imai@met.nagoya-u.ac.jp
Ikeda
Mitsuru
M
Enchi
Yukihiro
Y
Niimi
Takanaga
T
D000465:Algorithms; D001185:Artificial Intelligence; D017709:Fractals; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity