engleski

3D Surface Analysis and Classification in Neuroimaging Segmentation

Computing systems are key at all stages of neuroimaging, allowing scientists to control highly sophisticated imaging instruments and analyze vast amounts of complex data those instruments generate. Computing tools integrate an implementation of imaging instruments that capture signals from the brain, guide the behavioral tasks used to probe particular brain systems, reconstruct resulting signals into a three-dimensional representation of the brain, correct and suppress noise, statistically analyze the data, and visualize the results. The collected volumetric data can be segmented on many regions depending on our interest, and they are stored either in a data warehouse or a database where it is possible to query, compare or update it easily. This work emphasizes new algorithms for 3D edge and corner detection used in surface extraction and new concept of image segmentation in neuroimaging based on multidimensional shape analysis and classification. Using of NifTI standard for describing input data, enables interoperability and enhancement of existing computing tools used widely in neuroimaging research.

NIfTI; shape estimation; Canny detector; surface extraction; neuroimage segmentation

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