An Algorithm for Automatic Low-contrast Detection System and Its Evaluation for Images with Various Phantom Rotations

The purpose of this study is to develop an algorithm for automatic low-contrast object detection on the ACR 464 CT phantom and to investigate its evaluation for images with various phantom rotations. A software for automatic low-contrast detection was implemented with MATLAB R2013a. An algorithm was based on a template matching method. The reference points for the template matching method was centers of phantom and largest low-contrast object. The centers of the phantom and the largest low-contrast object were calculated using centroid formulae from the segmented phantom and objects with specific threshold values. Region of interests (ROIs) were located at each low-contrast object and background. The mean CT number and noise were calculated from pixel values within each ROI. The contrast-to-noise ratio (CNR) was then calculated based on the contrast between low-contrast object and background. The CNR cut-off is one. Object that has a CNR value more than the cut-off is considered resolved object and object that has a CNR value less than the cut-off is considered unresolved object. The algorithm was evaluated on images of the ACR CT phantom with various rotations of 0°, 22.5°, 45°, and 60°. Statistic evaluation was performed by ANOVA one-way to compare results of mean CT number, noise, and CNR for various rotations. The p-value more than 0.05 indicated that there is no significant difference. The proposed method was successful in placing ROIs at all low-contrast objects and at background for various phantom rotations. The CNR increases with the increase of size of the low-contrast object. The p-values for contrast, noise and CNR were 0.99, indicating that there are no statistically significant differences for various rotations. The minimum resolved object detectability in various rotations was 4 mm. An automatic technique for detecting low-contrast objects is accurate for various rotations.