Image-quality figure evaluator based on contrast-detail phantom in radiography.
BACKGROUND
In radiology, it is significantly important to produce adequate diagnostic information while minimally affecting the patient with the lowest amount of dose. A contrast-detail phantom is generally used to study the quality of image and the amount of radiation dose for digital X-ray imaging systems. To evaluate the quality of a phantom image, radiologists are traditionally required to manually indicate the location of the holes in each square in the phantom image. Then, the image quality figure (IQF) of the image can be evaluated. However, evaluation by the human eye is subjective as well as time-consuming, and it differs from person to person.


METHODS
In this paper, an image processing-based IQF evaluator is proposed to automatically measure the quality of a phantom image. Nine phantom images, each consisting of 2382 × 2212 pixels, were used as test images and were provided by Taichung Hospital, Department of Health, Executive Yuan, Taiwan, Republic of China. The IP-IQF evaluator separates the phantom image into squares and then stretches the contrast of each square to the range 0-255. After that, it splits each square into 3 × 3 equal-sized regions, and recognizes the pattern of the square based on the features computed by mean-difference gradient operation and run length enhancer. Furthermore, a genetic algorithm-based parameter values-detecting algorithm is presented to compute the optimal values of the parameters used in the IP-IQF evaluator.


RESULTS
The experimental results demonstrate that CoCIQ and the IP-IQF evaluator can efficiently measure the IQF of a phantom image. The IP-IQF evaluator is more effective than a radiologist and CoCIQ in evaluating the IQF of a phantom image.


CONCLUSIONS
The proposed IQF evaluator is more sensitive than not only the observation of radiologists but also the computer program CoCIQ. Moreover, a genetic algorithm is provided to compute the most suitable values of the parameters used in the IQF evaluator.
Department of Information Management, National Chin-Yi University of Technology, Taiwan, Republic of China.
Ching-Lin
CL
Wang
Chuin-Mu
CM
Wang
Yung-Kuan
YK
Chan
Rong-Tai
RT
Chen
D000465:Algorithms; D003287:Contrast Media / Q000494:pharmacology*; D004867:Equipment Design; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D019047:Phantoms, Imaging; D011786:Quality Control; D011871:Radiology / Q000379:methods; D015203:Reproducibility of Results; D012984:Software; D013624:Taiwan
D003287:Contrast Media