  White Matter Injury (WMI) is the most prevalent brain injury in the preterm
neonate leading to developmental deficits. However, detecting WMI in Magnetic
Resonance (MR) images of preterm neonate brains using traditional WM
segmentation-based methods is difficult mainly due to lack of reliable preterm
neonate brain atlases to guide segmentation. Hence, we propose a
segmentation-free, fast, unsupervised, atlas-free WMI detection method. We
detect the ventricles as blobs using a fast linear Maximally Stable Extremal
Regions algorithm. A reference contour equidistant from the blobs and the
brain-background boundary is used to identify tissue adjacent to the blobs.
Assuming normal distribution of the gray-value intensity of this tissue, the
outlier intensities in the entire brain region are identified as potential WMI
candidates. Thereafter, false positives are discriminated using appropriate
heuristics. Experiments using an expert-annotated dataset show that the
proposed method runs 20 times faster than our earlier work which relied on
time-consuming segmentation of the WM region, without compromising WMI
detection accuracy.
