A method for automatic low-contrast object segmentation for low-contrast detectability in CT images.

The purpose of this study is to develop software for automatic low-contrast objects segmentation in the ACR 464 computed tomography (CT) phantom and to evaluate the quantitative effect of radiation dose and object size on low-contrast detectability (LCD). The software was developed using MATLAB R2013a. The anchor coordinate of the largest low-contrast object (25 mm) was determined statistically by rotating a region of interest (ROI) of identical size over 360° in 1° angular increments to identify the coordinate corresponding to the maximum CT number. Meanwhile, the center of the phantom was determined based on a threshold-based method. The two center coordinates were used as references for detecting other low-contrast objects using a template matching. Regions of interests (ROIs) were automatically located within low-contrast objects and in the background, which is at the center of the phantom's image. Mean CT number, noise, contrast, and contrast-to-noise ratio (CNR) were subsequently computed. The low-contrast object detectability threshold was defined as a CNR cut-off of 1. The robustness of the anchor coordinate determination algorithm was evaluated across a range of imaging conditions, specifically targeting scenarios involving extreme noise levels and image tilting. Testing of the algorithm system was carried out on images scanned with various volume CT dose indexes (CTDIvols) of 21.4, 26.8, 32.1, 37.5, 42.8, and 53.6 mGy. The results were compared with a manual method (using micro DICOM viewer software) and statistical analysis of paired sample t-test between the results of automatic and manual methods was carried out. The results obtained using the automated methods indicate that the minimum resolved object sizes were 5, 5, 4, 4, 4, and 4 mm at CTDIvolvalues of 21.6, 26.8, 32.1, 37.5, 42.8, and 53.6 mGy, respectively. In comparison, the manual method yielded minimum resolved object sizes of 5, 5, 5, 4, 4, and 4 mm across the same CTDIvollevels, demonstrating a slight improvement in resolution for the automated approach at the 32.1 mGy dose level. An increase in CTDIvolaffected the increase in CNR. In conclusion, an automatic method for detecting low-contrast objects in the ACR 464 CT phantom was successfully completed. Low-contrast objects segmentation was shown to be accurate in test images.