Context-based morphometry in MRI imaging

Context-based morphometry (CBM) is an approach that allows analysis to be performed on an extended feature space that includes particular anatomical and physiological characteristics. Context can be defined as the circumstances, conditions or characteristics that constitute the environment in which something exists, occurs, or evolves. CBM may reveal relationships that are not spatially dependent and are therefore not detectable with typical image analysis techniques. For example, using a perfusion map, in a voxel-wise, i.e. voxel by voxel manner can improve results of image analysis. CBM is not limited to medical image derived data, and can be performed with any supplemental information that can be mapped to image space, even histological or immunological data. In this particular project we aim to generate a core morphometric features that will be used in brain studies. A particular study case will be proposed during the course of this project.
The developed feature extraction procedures will be included in the virtual laboratory SPINE. This will allow other scientist to use the core feature for other analysis.
Elaborate CBM techniques for morphometry of different brain structures using contextual information derived from:
- Distance maps
- Atlas
- 3D maps of neurotransmitter receptor
- Functional maps
- Native data
- Resampling matrices
- Image processing (ITK)
- Good level of Javascript. Knowledge on node.js (hapi server) is a plus
- Neuroimaging concepts are a plus
Charles Guttmann, 
Alfredo Morales Pinzon, 
- Angela L. Jefferson, Christopher M. Holland, David F. Tate, Istvan Csapo, Athena Poppas, Ronald A. Cohen, Charles R.G. Guttmann, Atlas-derived perfusion correlates of white matter hyperintensities in patients with reduced cardiac output,
Neurobiology of Aging, Volume 32, Issue 1, 2011, Pages 133-139
- Christopher M. Holland, Context-Based Morphometry of Disease Affecting the Cerebral White Matter: Perfusion and its Applications for Brain Parenchymal Damage, PhD dissertation, UMI Microform 3298646.