Ms. Grecia Eche Zapata Profile

Grecia Eche Zapata

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Proceedings Article | 2 March 2018 Paper

Automatic lung ultrasound B-line recognition in pediatric populations for the detection of pneumonia

G. Eche, O. Zenteno, B. Castaneda

Proceedings Volume 10574, 1057422 (2018) https://doi.org/10.1117/12.2293902

KEYWORDS: Ultrasonography, Video, Lung, Principal component analysis, Statistical analysis, Feature extraction, Image processing, X-rays, Scanners, Visualization

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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.

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