Pulmonary embolism detection on venous thrombosis ultrasound images with bi‐dimensional entropy measures: Preliminary results

Abstract Background Venous thromboembolism (VTE) is a common health issue. A clinical expression of VTE is a deep vein thrombosis (DVT) that may lead to pulmonary embolism (PE), a critical illness. When DVT is suspected, an ultrasound exam is performed. However, the characteristics of the clot observed on ultrasound images cannot be linked with the presence of PE. Computed tomography angiography is the gold standard to diagnose PE. Nevertheless, the latter technique is expensive and requires the use of contrast agents. Purpose In this article, we present an image processing method based on ultrasound images to determine whether PE is associated or not with lower limb DVT. In terms of medical equipment, this new approach (Doppler ultrasound image processing) is inexpensive and quite easy. Methods With the aim to help medical doctors in detecting PE, we herein propose to process ultrasound images of patients with DVT. After a first step based on histogram equalization, the analysis procedure is based on the use of bi‐dimensional entropy measures. Two different algorithms are tested: the bi‐dimensional dispersion entropy () mesure and the bi‐dimensional fuzzy entropy () mesure. Thirty‐two patients (12 women and 20 men, 67.63 ± 16.19 years old), split into two groups (16 with and 16 without PE), compose our database of around 1490 ultrasound images (split into seven different sizes from 32× 32 px to 128 × 128 px). p ‐values, computed with the Mann‐Whitney test, are used to determine if entropy values of the two groups are statistically significantly different. Receiver operating characteristic (ROC) curves are plotted and analyzed for the most significant cases to define if entropy values are able to discriminate the two groups. Results p ‐values show that there are statistical differences between of patients with PE and patients without PE for 112× 112 px and 128× 128 px images. Area under the ROC curve (AUC) is higher than 0.7 (threshold for a fair test) for 112× 112 and 128× 128 images. The best value of AUC (0.72) is obtained for 112× 112 px images. Conclusions Bi‐dimensional entropy measures applied to ultrasound images seem to offer encouraging perspectives for PE detection: our first experiment, on a small dataset, shows that on 112× 112 px images is able to detect PE. The next step of our work will consist in testing this approach on a larger dataset and in integrating in a machine learning algorithm. Furthermore, this study could also contribute to PE risk prediction for patients with VTE.