Early detection of white matter injury in premature newborns can facilitate timely clinical treatments reducing the potential risk of later developmental deficits. It was reported that there were more than 5% premature newborns in British Columbia, Canada, among which 5-10% exhibited major motor deficits and 25-50% exhibited significant developmental and visual deficits. With the advancement of computer assisted detection systems, it is possible to automatically identify white matter injuries, which are found inside the grey matter region of the brain. Atlas registration has been suggested in the literature to distinguish grey matter from the soft tissues inside the skull. However, our subjects are premature newborns delivered at 24 to 32 weeks of gestation. During this period, the grey matter undergoes rapid changes and differs significantly from one to another. Besides, not all detected white spots represent injuries. Additional neighborhood information and expert input are required for verification. In this paper, we propose a white matter feature identification system for premature newborns, which is composed of several steps: (1) Candidate white matter segmentation; (2) Feature extraction from candidates; (3) Validation with data obtained at a later stage on the children; and (4) Feature confirmation for automated detection. The main challenge of this work lies in segmenting white matter injuries from noisy and low resolution data. Our approach integrates image fusion and contrast enhancement together with a fuzzy segmentation technique to achieve promising results. Other applications, such as brain tumor and intra-ventricular haemorrhage detection can also benefit from our approach.