Local intensity model: An outlier detection framework with applications to white matter hyperintensity segmentation

Automatic segmentation of white matter hyperintensities (WMH) from T2-Weighted and FLAIR MRI is a common task that needs to be performed in the analysis of many different diseases. A method to segment the WMH is proposed whereby a local intensity model (LIM) of normal tissue is generated. WMH are detected as outliers from this model. The LIM enables an accurate modeling of intensity variations thus reducing false positives. Moreover only scans with normal tissues are required to create the model. Twelve normal scans were used to generate the LIM and validation was conducted on a set of 46 scans. Similarity indices between the proposed approach and manual segmentations were 0.59±0.15, 0.65±0.08 and 0.77±0.08 for subjects with small, moderate and large volume of lesions respectively. The proposed approach performed better than support vector machines on the same dataset and compared favorably to approaches in literature.