Kyle Marcolini MRI Scan Classification

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

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

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)

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

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)

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)

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

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