Warangkana Weerawanich1,2, Mayumi Shimizu3, Yohei Takeshita4, Kazutoshi Okamura1, Shoko Yoshida5, Kazunori Yoshiura1. 1. 1 Department of Oral and Maxillofacial Radiology, Faculty of Dental Science, Kyushu University , Kyushu University , Fukuoka , Japan. 2. 2 Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Mahidol University , Mahidol University , Bangkok , Thailand. 3. 3 Department of Oral and Maxillofacial Radiology, Kyushu University Hospital , Kyushu University Hospital , Fukuoka , Japan. 4. 4 Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences , Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences , Okayama , Japan. 5. 5 Section of Image Diagnostics, Department of Diagnostics and General Care, Fukuoka Dental College , Fukuoka Dental College , Fukuoka , Japan.
Abstract
OBJECTIVES: (1) To develop an observer-free method of analysing image quality related to the observer performance in the detection task and (2) to analyse observer behaviour patterns in the detection of small mass changes in cone-beam CT images. METHODS: 13 observers detected holes in a Teflon phantom in cone-beam CT images. Using the same images, we developed a new method, cluster signal-to-noise analysis, to detect the holes by applying various cut-off values using ImageJ and reconstructing cluster signal-to-noise curves. We then evaluated the correlation between cluster signal-to-noise analysis and the observer performance test. We measured the background noise in each image to evaluate the relationship with false positive rates (FPRs) of the observers. Correlations between mean FPRs and intra- and interobserver variations were also evaluated. Moreover, we calculated true positive rates (TPRs) and accuracies from background noise and evaluated their correlations with TPRs from observers. RESULTS: Cluster signal-to-noise curves were derived in cluster signal-to-noise analysis. They yield the detection of signals (true holes) related to noise (false holes). This method correlated highly with the observer performance test (R2 = 0.9296). In noisy images, increasing background noise resulted in higher FPRs and larger intra- and interobserver variations. TPRs and accuracies calculated from background noise had high correlation with actual TPRs from observers; R2 was 0.9244 and 0.9338, respectively. CONCLUSIONS: Cluster signal-to-noise analysis can simulate the detection performance of observers and thus replace the observer performance test in the evaluation of image quality. Erroneous decision-making increased with increasing background noise.
OBJECTIVES: (1) To develop an observer-free method of analysing image quality related to the observer performance in the detection task and (2) to analyse observer behaviour patterns in the detection of small mass changes in cone-beam CT images. METHODS: 13 observers detected holes in a Teflon phantom in cone-beam CT images. Using the same images, we developed a new method, cluster signal-to-noise analysis, to detect the holes by applying various cut-off values using ImageJ and reconstructing cluster signal-to-noise curves. We then evaluated the correlation between cluster signal-to-noise analysis and the observer performance test. We measured the background noise in each image to evaluate the relationship with false positive rates (FPRs) of the observers. Correlations between mean FPRs and intra- and interobserver variations were also evaluated. Moreover, we calculated true positive rates (TPRs) and accuracies from background noise and evaluated their correlations with TPRs from observers. RESULTS: Cluster signal-to-noise curves were derived in cluster signal-to-noise analysis. They yield the detection of signals (true holes) related to noise (false holes). This method correlated highly with the observer performance test (R2 = 0.9296). In noisy images, increasing background noise resulted in higher FPRs and larger intra- and interobserver variations. TPRs and accuracies calculated from background noise had high correlation with actual TPRs from observers; R2 was 0.9244 and 0.9338, respectively. CONCLUSIONS: Cluster signal-to-noise analysis can simulate the detection performance of observers and thus replace the observer performance test in the evaluation of image quality. Erroneous decision-making increased with increasing background noise.
Authors: K Yoshiura; H Stamatakis; X Q Shi; U Welander; W D McDavid; J Kristoffersen; G Tronje Journal: Dentomaxillofac Radiol Date: 1998-05 Impact factor: 2.419
Authors: Kalpana M Kanal; Jonathan H Chung; Jin Wang; Puneet Bhargava; Jennifer R Kohr; William P Shuman; Brent K Stewart Journal: AJR Am J Roentgenol Date: 2011-08 Impact factor: 3.959