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We present an automated generic methodology for symmetry identification and asymmetry quantification, novel method of identifying and delineation of brain pathology by analyzing the opposing sides of the brain utilizing of inherent left-right symmetry in the brain. After symmetry axis has been detected, we apply non-parametric statistical tests operating on the pairs of samples to identify initial seeds points which is defined defined as the pixels where the most statistically significant difference appears. Local region growing is performed on the difference map, from where the seeds are aggregating until it captures all 8-way connected high signals from the difference map. We illustrate the capability of our method with examples ranging from tumors in patient MR data to animal stroke data. The validation results on Rat stroke data have shown that this approach has promise to achieve high precision and full automation in segmenting lesions in reflectional symmetrical objects.

Enhanced techniques for asymmetry quantification in brain imagery