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We propose a novel cerebellum segmentation method for MRI, based on a combination of statistical models of the structure's expected location in the brain and its local appearance. The appearance model is obtained from a k-nearest-neighbor classifier, which uses a set of multi-scale local image descriptors as features. The spatial model is constructed by registering multiple manually annotated datasets...
We present improvements to a recently proposed model-based segmentation strategy, called Clouds. We demonstrate the usefulness of the new model Cloud Bank in the automatic MR-image segmentation of the brain (without medulla and spinal cord), the cerebellum, and each brain hemisphere. These structures are connected in several parts, imposing serious challenges for segmentation. The results indicate...
This paper reports a novel method for fully automated segmentation of rodent brain volume by extending the robust active shape models to incorporate an automatic prior shape selection process. This automatic prior shape selection process using support vector machines provides an automatic shape initialization method for further segmentation of rodent brain structures such as Cerebellum, Neocortex,...
Unlocking the neural code of the human's brain has long been the main focus of neuroscience studies with intensive research carried out to model the brain. Some of these techniques use the magnetic resonance imaging (MRI) and electroencephalographic (EEG) systems. The EEG signals estimate the cortical activities using non-invasive Brain Computer Interfaces (BCI) with scalp potential measurements targeted...
We present a semi-automatic segmentation technique of the anatomical structures of the brain: cerebrum, cerebellum, and brain stem. The method uses graph cuts segmentation with an anatomic template for initialization. First, a skull stripping procedure is applied to remove non-brain tissues. Then, the segmentation is done hierarchically by first, extracting first the cerebrum from the brain, and then...
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