Pipelines vs Imaging Capabilities Sample Clauses

Pipelines vs Imaging Capabilities. The following table gives a list of popular toolkits and corresponding image processing capabilities used to respectively normalize data, convert image files, anonymize data, extract features from within images and process statistics. This classification aims to introduce the notion of categories, that resulting neuGRID system could use to sort out year 2 gridified algorithms portfolio (see D10.2 at Month 22). Main Category Type of Processing Pipeline / Algorithm Toolkit Pre & Intermediar Normalization Linear and nonlinear (correction factors) SPM Segmentation (voxels labelling priors-based) SPM y Processing Warping (sulci based) LoNI Warping (intensity based) MNI File Conversion Dicom to MINC MNI Dicom to Analyze MNI MRIcro Anonymization Face Scrambling LoNI Pseudonymization -- Research Segmentation Cortical Density SPM LoNI Cortical Thickness LoNI Hippocampus Atrophy (shrinkage) LoNI Hippocampus Volume MNI XxXX Xxxxx Matter Volume and Distribution IdeALab Cortical Thickness MNI Statistics Cross Population Patterns -- Diagnostic Segmentation Cortical Density SPM Cortical Contour Drawing + Voxels Counting MNI White Matter Age Related Scale (Wahlund) -- Regional Brain Metabolism Alterations HERMES While this categorization will certainly gain in clarity and structuring when formalized as part of the requirements analysis, it is already clear that algorithms/ pipelines can be classified whether they are used to (1) convert, (2) normalize, (3) anonymize data or to (4) extract meaningful measurements through imaging segmentation and (5) process statistical analyses. Beyond classification and along the lines of thus far gathered requirements, this wide variety of toolkit utilities indicates that there is a generalisable pipeline model. Clearly, four steps tend to shape, as illustrated in figure 5 below (and as is verifiable in end-users‟ process descriptions – see Appendix A – SPM Pipeline process for a concrete example).
Sample 1
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