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Final Project Proposal EEN538

MRI Scan Classification. Kyle Marcolini. Final Project Proposal EEN538. Previous Research . For EEN653, project devised based on custom built classifier for demented MRI brain scans Minimal processing methods implemented in preprocessing/segmentation stage

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Final Project Proposal EEN538

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  1. MRI Scan Classification Kyle Marcolini Final Project ProposalEEN538

  2. Previous Research • For EEN653, project devised based on custom built classifier for demented MRI brain scans • Minimal processing methods implemented in preprocessing/segmentation stage • Minimal features extracted based on image characteristics • Classifier was ~80-90% accurate in determining demented brain scans

  3. Proposal • Using previously built classifier and scan database, implement methods for preprocessing and feature extraction • Attempt to increase classification accuracy without changing the classifier • Focus solely on processing of the scans

  4. Brain Database • Oasis brain database • Each file contains brain scan and text file, which contains: • Info on scanned subject (age, sex) • MMSE score (cognitive impairment test) • CDR (rating of dementia) • Final categorization of (either none, slight, or full-on dementia)

  5. Previous Preprocessing Methods • Increase the brightness and contrast by a predetermined factor for all scans • Set threshold levels to diminish 256 possible intensity levels to 5 • This intruduced a lot of noise • No further noise removal, average, or smoothing • Resulting images varied in brightness and threshold level

  6. Previous Features Extracted • Image mean (average intensity value) • Symmetry (healthy brains tend to be more symmetrical) • Gradient mean and variance (edges of brain) • Normalized black area in hippocamous (center black area, usually darker means more dementia)

  7. Proposed Processing Methods • Adaptive brightness and contrast based on prior scan’s color mean • Averaging filter to remove unwanted noise pixels • Deblurring for darker scanse with a lot of inherent noise and blur • Better thresholding (still thinking of better way than select ranges of intensities to round to a single value)

  8. Additional Features • Incorporated some prior features applied to filtered scans, the gradients of the scans • Frequency-based analysis rather than just spatial • Incorporate wavelet transform-based features • Detection techniques to search for abnormalities or potential lesions

  9. Predicitions • The k-nearest neighbor classifier from before successfully classified many of the noisy scans • With filtered and consistent scans, I hope to achieve a program that is consistently greater than 95% accurate

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