Share Email Print
cover

Proceedings Paper

An explorative childhood pneumonia analysis based on ultrasonic imaging texture features
Author(s): Omar Zenteno; Kristians Diaz; Roberto Lavarello; Mirko Zimic; Malena Correa; Holger Mayta; Cynthia Anticona; Monica Pajuelo; Richard Oberhelman; William Checkley; Robert H. Gilman; Dante Figueroa; Benjamín Castañeda
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

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 Niño (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.

Paper Details

Date Published: 22 December 2015
PDF: 6 pages
Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 968112 (22 December 2015); doi: 10.1117/12.2207944
Show Author Affiliations
Omar Zenteno, Pontificia Univ. Católica del Perú (Peru)
Kristians Diaz, Pontificia Univ. Católica del Perú (Peru)
Roberto Lavarello, Pontificia Univ. Católica del Perú (Peru)
Mirko Zimic, Univ. Peruana Cayetano Heredia (Peru)
Malena Correa, Univ. Peruana Cayetano Heredia (Peru)
Tulane Univ. (United States)
Holger Mayta, Univ. Peruana Cayetano Heredia (Peru)
Tulane Univ. (United States)
Cynthia Anticona, Univ. Peruana Cayetano Heredia (Peru)
Tulane Univ. (United States)
Monica Pajuelo, Univ. Peruana Cayetano Heredia (Peru)
Tulane Univ. (United States)
Richard Oberhelman, Tulane Univ. (United States)
William Checkley, Johns Hopkins Univ. (United States)
Robert H. Gilman, Johns Hopkins Univ. (United States)
Dante Figueroa, Instituto Nacional de Salud del Niño (Peru)
Benjamín Castañeda, Pontificia Univ. Católica del Perú (Peru)


Published in SPIE Proceedings Vol. 9681:
11th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Juan D. García-Arteaga; Jorge Brieva, Editor(s)

© SPIE. Terms of Use
Back to Top