Feature Extraction and Classification to Diagnose Hypoxic-Ischemic Encephalopathy Patients by Using Susceptibility-Weighted MRI Images

In this paper a method is presented to enable automatic classification of the degree of abnormality of susceptibility-weighted images (SWI) acquired from babies with hypoxic-ischemic encephalopathy (HIE), in order to more accurately predict eventual cognitive and motor outcomes in these infants. SWI images highlight the cerebral venous vasculature and can reflect abnormalities in blood flow and oxygenation, which may be linked to adverse outcomes. A qualitative score based on magnetic resonance imaging (MRI) analyses is assigned to SWIs by specialists to determine the severity of abnormality in an HIE patient. The method allows the detection of image ridges, representing the vessels in SWIs, and the histogram of the ridges grey scales. A curve with only four parameters is fitted to the histograms. These parameters are then used to estimate the SWI abnormality score. The images are classified by using a kNN- and multiple SVM classifiers based on the parameters of the fitting curves. The algorithm is tested on an SWI-MRI dataset consisting of 10 healthy infants and 48 infants with HIE with a range of SWI abnormality scores between 1 and 7. The accuracy of classifying babies with HIE vs. those without (i.e.: healthy controls) using our algorithm with a leave-one-out strategy is measured as 91.38%. Our method is fast and could increase the prognostic value of these scans, thereby improving management of the condition, as well as elucidating the disease mechanisms of HIE.