An Algorithm for Automatic Low-contrast Detection System and Its Evaluation for Images with Various Phantom Rotations (International Journal of Scientific Research in Science and Technology, 2024)
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.