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Automatic segmentation of brain infarction in diffusion-weighted MR images

It is important to detect the site and size of infarction volume in stroke patients. An automatic method for segmenting brain infarction lesion from diffusion weighted magnetic resonance (MR) images of patients has been developed. The method uses an integrated approach which employs image processing techniques based on anisotropic filters and atlas-based registration techniques. It is a multi-stage process, involving first images preprocessing, then global and local registration between the anatomical brain atlas and the patient, and finally segmentation of infarction volume based on region splitting and merging and multi-scale adaptive statistical classification. The proposed multi-scale adaptive statistical classification model takes into account spatial, intensity gradient, and contextual information of the anatomical brain atlas and the patient. Application of the method to diffusion weighted imaging (DWI) scans of twenty patients with clinically determined infarction was carried out. It shows that the method got a satisfied segmentation even in the presence of radio frequency (RF) inhomogeneities. The results were compared with lesion delineations by human experts, showing the identification of infarction lesion with accuracy and reproducibility.