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Detecting brain structural changes as biomarker from magnetic resonance images using a local feature based SVM

Ye Chen1, Judd Storrs, Lirong Tan

  • 1Division of Biomedical Informatics, Cincinnati Children's Hospital Research Foundation, 3333 Burnet Avenue, Cincinnati, OH 45229-3026, United States(1); School of Electronics and Computing Systems, University of Cincinnati, 497 Rhodes Hall, Cincinnati, OH 45221, United States.

Journal of Neuroscience Methods
|September 18, 2013
PubMed
Summary

This study introduces a novel machine learning method using local features from magnetic resonance (MR) images to detect brain structural changes. This approach aids in the early diagnosis of neurological diseases without complex registration, achieving high classification accuracy.

Keywords:
BiomarkerBrainLocal featuresMRI imagesNeurological diseasesPsychiatric diseasesSVM

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Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).