Automatic brain image segmentation for evaluation of experimental ischemic stroke using gradient vector flow and kernel annealing

Ischemic stroke is the most prevalent catastrophic disease of the brain. Various animal models have been used to study the disease. The majority of the models are based on induction of focal ischemic cerebral necrosis, followed by exhaustive morphometric analysis of the tissues. Despite recent advances in machine learning and image processing, neurological damage evaluations are still based on tedious manual or semi-automatic segmentation of brain images. We demonstrate a method that uses active contours combined with a kernel annealing approach to automatically segment the brain organs of interest, as well as a simple feature that highlights the contrast between normal and infarct brain tissue for automated analysis. The automated segmentation and analysis solution will be useful for increasing the productivity of experimentation and removing investigator bias from the data analysis.