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The backscatter coefficient (BSC) quantifies the frequency-dependent reflectivity of tissues. Accurate estimation of the BSC requires knowledge of the attenuation coefficient slope (ACS) of tissues in the beam path between the transducer and the insonified region of interest, namely, the total attenuation. In this study, the total attenuation is calculated as the cumulative sum of values of a local attenuation map devised using full angular spatial compounding (FASC). The BSC was parameterized through the integrated backscatter coefficient (iBSC) obtaining iBSC maps. Experimental validation of the proposed approach consisted of scanning two cylindrical physical phantoms with off-centered inclusions having different ACS and BSC values than the background. Additional iBSC maps were computed assuming an uniform ACS map of 0.5 dB/cm/MHz (which is a value assumed for soft tissues) instead of the FASC-ACS map. Finally a iBSC map was obtained using an ideal ACS map formed with ground truth ACS values and knowledge of inclusion true position. The results were comparable when using the FASC-ACS map or the ideal ACS map in term of inclusion detectability and estimation accuracy. The use of the uniform ACS map resulted in some cases with very high fractional error (>;9 dB), which highlights the relevance of accurate compensation for total attenuation. These results suggest that BSCs can be reliably estimated using total attenuation compensation from FASC-ACS maps.

Pneumonia is one of the most common acute respiratory infections among pediatric populations worldwide. Ultrasound is becoming increasingly important in the diagnosis of lung diseases as a more portable and safer alternative to X-ray imaging. In the current work, we present a new automatic system for detection of B-lines, one of the distinctive features of pneumonic ultrasound scans, using amplitude modulation-frequency modulation (AM-FM). Features are evaluated on 109 videos obtained from 100 pediatric patients using a Verasonics V1 scanner. Further, the results were compared to the ones obtained with a previously published spectral feature (SF) method. Sensitivities of 92% and 83% and specificities of 91% and 70% were obtained on zone-1 and zone-2 of the lungs, respectively. In contrast, the SF method provided sensitivities of 72% and 68% in zone-1 and zone-2, respectively, and specificities of 68% and 46% in zone-1 and zone-2, respectively. In addition, the AM-FM method allowed increasing the F1-score when compared to the SF method from 70% to 87% and from 61% to 78% in zone-1 and zone-2, respectively. The results suggest the proposed method may be useful for the computer assisted diagnosis of pneumonia.

Pneumonic lung sonograms are known to include vertical comet-tail artifacts called B-lines. In this study, the potential of histogram properties from lung ultrasound images for the automatic identification of B-line artifacts is explored. Five histogram features (skewness, kurtosis, standard deviation, energy and average) were calculated for intercostal spaces. The sample consisted of 15 positive- and 15 negative-diagnosed B-mode videos selected by a medical expert and captured in a local pediatric health institute. For each frame, an initial domain of interest (DOI) starting from the pleural line is automatically outlined. The pleura is detected by a brightness based thresholding. Smaller regions containing the intercostal spaces inside the DOI are then outlined and histogram features are estimated. The potential classification of properties was evaluated independently, in pairs and using the group of 5. For single feature analysis, the optimal threshold was selected based on ROC (receiver operator characteristic) curve. For studying features in pairs a support vector machine (SVM) analysis using a RBF kernel was performed. Finally, for studying the five features, PCA (principal component analysis) was useful to determine the two principal components and apply an algorithm able to identify a B-line in the intercostal space. The results revealed that energy performed best as discriminator when using a single feature with 77% sensitivity, 75% specificity and 75% accuracy. When using features in pairs, average and skewness performed best with 93% sensitivity, 86% specificity and 88% accuracy. Finally, analyzing the 5 features, the results were 100% sensitivity, 98% specificity and 98% accuracy.

According to World Health Organization, pneumonia is the respiratory disease with the highest pediatric mortality rate accounting for 15% of all deaths of children under 5 years old worldwide. The diagnosis of pneumonia is commonly made by clinical criteria with support from ancillary studies and also laboratory findings. Chest imaging is commonly done with chest X-rays and occasionally with a chest CT scan. Lung ultrasound is a promising alternative for chest imaging; however, interpretation is subjective and requires adequate training. In the present work, a two-class classification algorithm based on four Gray-level co-occurrence matrix texture features (i.e., Contrast, Correlation, Energy and Homogeneity) extracted from lung ultrasound images from children aged between six months and five years is presented. Ultrasound data was collected using a L14-5/38 linear transducer. The data consisted of 22 positive- and 68 negative-diagnosed B-mode cine-loops selected by a medical expert and captured in the facilities of the Instituto Nacional de Salud del Nino (Lima, Peru), for a total number of 90 videos obtained from twelve children diagnosed with pneumonia. The classification capacity of each feature was explored independently and the optimal threshold was selected by a receiver operator characteristic (ROC) curve analysis. In addition, a principal component analysis was performed to evaluate the combined performance of all the features. Contrast and correlation resulted the two more significant features. The classification performance of these two features by principal components was evaluated. The results revealed 82% sensitivity, 76% specificity, 78% accuracy and 0.85 area under the ROC.