We further analyzed the variability of change of scores of expert radiologists for each subject. For this, we constructed Bland-Altman plot [40] where the limits of observer agreements were indicated by bias ± 1.96 std (bias: average difference, std: standard deviation). In Bland-Altman plot, the difference of the performances was plotted against the average of the performances as shown in Fig. 10(b) . It was noted from this figure that the largest disagreement of the scoring between observers To obtain an overall computer score from the proposed CAD system, on the other hand, TIB regions detected by the CAD system were first labeled automatically during the detection process. Then, a computer score was calculated by averaging the volume occupied by the labeled TIB regions over the whole lung volume. Calculated computer score was then normalized to fit the visual grading scheme explained in Section IV-C. Linear regression model was fitted to all subjects' scores both from computer and the consensus scores of the participating radiologists (DMJ and AW) and Pearson product-moment correlation coefficient was computed for this model. A scatter plot of the linear regression model and the computer-observer agreement correlation is shown in Fig. 11 . It is clear from this plot that visual and quantitative assessments correlate well as indicated by the Pearson product-moment correlation of R 2 = 0.824 (p < 0.01). Finally, we illustrate an example of TIB and non-TIB region classification by expert annotation and computer quantification by our proposed method in Fig. 12(a) and (b) , respectively.