Fetal lung maturity analysis using ultrasound image features

Abstract

This pilot study was carried out to find the feasibility of analyzing the maturity of the fetal lung using ultrasound images. Data were collected from normal pregnant women at intervals of two weeks from the gestation age of 24 to 38 weeks. Images were acquired at two centers located at different geographical locations. The total data acquired consisted of 750 images of immature and 250 images of mature class. A region of interest of 64 x 64 pixels was used for extracting the features. Various textural features were computed from the fetal lung and liver images. The ratios of fetal lung to liver feature values were investigated as possible indexes for classifying the images into those from mature (reduced pulmonary risk) and immature (possible pulmonary risk) lung. The features used are fractal dimension, lacunarity, and features derived from the histogram of the images. The following classifiers were used to classify the fetal lung images as belonging to mature or immature lung: nearest neighbor, k-nearest neighbor, modified k-nearest neighbor, multilayer perceptron, radial basis function network, and support vector machines. The classification accuracy obtained for the testing set ranges from 73% to 96%.

Fractal Physiology, Breath-to-Breath Variability and Respiratory Diseases: An Introduction to Complex Systems Theory Application in Pulmonary and Critical Care Medicine

“…Peng and co-workers [53] showed that aging was associated with a breakdown of fractal dynamics of respiratory signals via a decrease in β slope towards 0.5 (randomness). Concerning early stages of development in humans, one study [54] has found that ultrasonographic patterns for assessment lung maturity showed fractal properties with a power law behavior. In addition, the β slope increased with gestational age from 28 to 38 weeks.…”

A fast automatic recognition and location algorithm for fetal genital organs in ultrasound images

“…The number of features was reduced based on minimizing the classification error and an average correlation coefficient, and a feedforward neural network was implemented to classify the 5 selected features. Bhanu Prakash et al(2002) analyzed the maturity of fetal lungs using US image recognition. The ROIs were manually predefined and 6 features were selected using Pearson's correlation test from a total of 64 features which were extracted from 5 textural measurements, then the performances of 6 classifiers upon the selected features were compared.…”

Feasibility and Reproducibility of Fetal Lung Texture Analysis by Automatic Quantitative Ultrasound Analysis and Correlation with Gestational Age

“…There are few references in the literature on the assessment of fetal lung texture by quantitative ultrasound tissue characterization. Although different methodologies have been proposed, they all report differences in lung texture features along gestation when compared to the reflection pattern of liver [20,21,22,23,24]. …”