Tuberculosis detection on chest X-rays using two-dimensional multiscale symbolic dynamic entropy

Several radiological patterns associated with pulmonary tuberculosis (TB) have been identified on chest X-rays (CXR) used for screening purposes. As a result, several automatic computational tools have emerged for this purpose. We propose a new algorithm, two-dimensional multiscale symbolic dynamic entropy (MSDE${}_{2D}$), to develop a computational tool sensitive to these subtle patterns variations and noise robustness for evaluating CXR images from healthy and TB-diagnosed individuals. The one-dimensional SDE algorithm was previously shown to be more efficient in detecting amplitude variations and in computational calculations (compared to other entropy algorithms). Additionally, we also extracted first-order statistical parameters like standard deviation (SD), and mean of positive pixels (MPP), among others. These MSDE${}_{2D}$ and first-order texture features were used to detect TB in each lung individually. The MSDE${}_{2D}$ was validated using a synthetic dataset and optimized for the best set of parameters. We verified that, for both lungs, the MSDE${}_{2D}$ values were significantly different between healthy and TB CXR images ($P<0.05$), and the effect size was $|$d$|$ $>$0.23. From the first-order parameters, only the mean, SD, entropy, and MPP were statistically different between both groups for the left lung ($P<0.05$; $|$d$|$ $>$0.22). For the right lung, all first-order features significantly differentiated TB patients ($P<0.05$; $|$d$|$ $>$0.28). Finally, we show that a multi-layer perceptron obtained 86.4 and 85.2% accuracy in detecting TB in the left and right lungs, respectively. The highest sensitivity values achieved in this study were 71.4% and 81.8% for the left and right lungs, respectively.