Evaluation of semiautomated quantification of cranial ultrasound images in newborns as a predictor of Neonatal Behavioral Assessment Scale

Diagnosis of white matter damage by neonatal cranial ultrasound (CrUS) is subject to inter-observer variability and has a low sensitivity to detect late abnormal neurodevelopment in life. In the last decades there have been a significant effort reporting that statistical features of ultrasound image...

Full description

Saved in:
Bibliographic Details
Main Authors: Bonet-Carne, E, Tenorio, V, Figueras, F, Gratacos, E, Amat-Roldan, I
Format: Conference Proceeding
Language:English
Subjects:
Behavioral science
Biomedical imaging
Feature extraction
Image texture analysis
Pattern Analysis
Pediatrics
Pixel
Statistical Learning
Training
Ultrasonic imaging
Visualization
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Diagnosis of white matter damage by neonatal cranial ultrasound (CrUS) is subject to inter-observer variability and has a low sensitivity to detect late abnormal neurodevelopment in life. In the last decades there have been a significant effort reporting that statistical features of ultrasound images carry important information associated with changes of tissue microstructure. In this work we explored the ability of a semi-automated image processing method to associate ultrasound texture patterns with Neonatal Behavioral Assessment Scale (NBAS) performance in premature neonates. A total of ninety infants born at a median gestational age of 29 weeks were included. The infants underwent one CrUS scan performed at the same day that NBAS test. In this work, we developed a feature selection algorithm to identify combination of features that correlated to NBAS clusters. Our algorithm was then able to predict individual underscored NBAS clusters with accuracy higher than 80% in a "blind" sample.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2011.5872350