MRI parallel processing for embedded visualization

This work focuses on the development of the BrainTool, an embedded application that provides full automated volume segmentation and visualization of a reconstructed 3D mesh of human brains. A brain scan is performed to acquire a set of voxels using a 3D Magnetic Resonance sampler. The application is executed on embedded (mobile) systems. The tool operates through three principal modules: an automatic segmentation to remove the non-brain structures, a parallel implementation state of art Marching Cubes mesh synthesizer followed by an OpenGL ES rendering step to achieve model interactive visualization. The development of the application is done using two different low cost boards integrating low power state of art CPU and GPU into embedded system on chip; in order to test the solution, samples acquired from MRI equipment commonly available at hospitals are used. The experiments allow to conclude that the Marching Cube algorithm parallelize fairly enough and can be distributed on the available resources reaching scalable performances when executed on laptop and embedded systems. Rendering of complex graphics assets up to 2.7M triangles per model is performed.