A Non-Parametric Approach to Automatic Change Detection in MRI Images of the Brain

We present a novel approach to change detection between two brain MRI scans (reference and target.) The proposed method uses a single modality to find subtle changes; and does not require prior knowledge (learning) of the type of changes to be sought. The method is based on the computation of a local kernel from the reference image, which measures the likeness of a pixel to its surroundings. This kernel is then used as a feature and compared against analogous features from the target image. This comparison is made using cosine similarity. The overall algorithm yields a scalar dissimilarity map (DM), indicating the local statistical likelihood of dissimilarity between the reference and target images. DM values exceeding a threshold then identify meaningful and relevant changes. The proposed method is robust to various challenging conditions including unequal signal strength.

• Type: Conference
• Year: 2009
• Where: ISBI
• Authors: Hae Jong Seo, Peyman Milanfar
• Keywords: Brain Mri Scans, ISBI 2009, Medical Imaging, Subtle Changes, Target Images