Voxel-Based Morphometry (VBM) is the process of measuring volumetric changes in structural imagery. We propose to characterise different algorithms that measure the accuracy at the scale of gross morphological structures. For images to be studied, the brains have to be labelled first. Before registrations, images are usually skull-stripped, leaving only the brain in the image. Images are then linearly registered using FSL software. Rigid registration is usually using a standard MNI152 template. (Klein et al. 2009) used volume and surface overlap, volume similarity, and distance measures to evaluate how well individual anatomical regions, as well as total brain volumes, register to one another. Metrics for measuring differences in algorithm performance can be average brain volume, grey matter overlap, white matter overlap, correlation of a measure of curvature, local measures of distance and shape between corresponding principal sulci. In conclusion, (Klein et al. 2009) mentioned that the results of comparisons were better or comparable with skull-stripped images.
MR and functional MR image analysis can be a significant portion of the diagnoses of psychological related diseases. One such disease is Autism. When the most significant regions regarding specific condition are identified, appropriate machine learning algorithms can be applied for its analysis.
Duration: 36 Months
Deadline to Apply: 19 January 2020
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