Ridge Detection and Analysis of Susceptibility-Weighted Magnetic Resonance Imaging in Neonatal Hypoxic-Ischaemic Encephalopathy

Abstract

The purpose of this study is to develop a new automated system to classify susceptibility weighted images (SWI) obtained to evaluate neonatal hypoxic-ischaemic injury, by detecting and analyzing ridges within these images. SW images can depict abnormal cerebral venous contrast as a consequence of abnormal blood flow, perfusion and thus oxygenation in babies with HIE. In this research, a dataset of SWI-MRI images, acquired from 42 infants with HIE during the neonatal period, features are obtained based on ridge analysis of SW images including the width of blood vessels, the change in intensity of the veins’ pixels in comparison with neighboring pixels, the length of blood vessels and Hessian eigenvalues for ridges are extracted. Normalized histogram parameters in the single or combined features are used to classify SWIs by $kNN$ and random forest classifiers. The mean and standard deviation of the classification accuracies are derived by randomly selecting 11 datasets ten times from those with normal neurological outcome (n = 31) at age 24 months and those with abnormal neurological outcome (n = 11), to avoids classification biases due to any imbalanced data. The feature vectors containing width, intensity, length and eigenvalue show a promising classification accuracy of 78.67% $\pm$ 2.58%. The features derived from the ridges of the blood vessels have a good discriminative power for prediction of neurological outcome in infants with neonatal HIE. We also employ Support Vector Regression (SVR) to predict the scores of motor and cognitive outcomes assessed 24 months after the birth. Our mean relative errors for cognitive and motor outcome scores are 0.113 $\pm$ 0.13 and 0.109 $\pm$ 0.067 respectively.