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A non-parametric approach to automatic change detection in MRI images of the brain

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2 Author(s)
Hae Jong Seo ; Electr. Eng. Dept., Univ. of California at Santa Cruz, Santa Cruz, CA, USA ; Milanfar, P.

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.

Published in:

Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on

Date of Conference:

June 28 2009-July 1 2009