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Computational BioMedical Informatics
SCE 5095: Special Topics Course
Instructor: Jinbo Bi
Computer Science and Engineering Dept.
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Course Information
Instructor: Dr. Jinbo Bi – Office: ITEB 233– Phone: 860-486-1458
– Email: [email protected]
– Web: http://www.engr.uconn.edu/~jinbo/– Time: Tue / Thu. 3:30-4:45pm – Location: CAST 201– Office hours: Tue. 2:30-3:30pm
HuskyCT– http://learn.uconn.edu– Login with your NetID and password
– Illustration
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Summary of topics in clustering
Discussed different types of clusterings, and different cluster types
Introduced k-means Introduced hierarchical clustering, particularly the
bottom-up approaches, focused on intra-cluster distance/similarity design
Introduced spectral clustering, local behaviors Started to look at a medical problem where
clustering techniques can be applied
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Application in medical informatics
Anatomy of the heart Cardiac ultrasound videos (clips) 2-D view recognition problem Diagram of building an informatics system
– Preprocessing (normalization, fan detection)– Feature calculation– Clustering– Validation
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Ultrasound Clips
Parasternal long-axis view, parasternal short-axis view, apical 4-chamber view, apical 2-chamber view– A healthy hearthttp://www.youtube.com/watch?v=7TWu0_Gklzo&feature=related
– An abnormal heart (dilated cardiomyopathy)http://www.youtube.com/watch?v=37KDMNiV3AU&feature=related
– Abnormal heart (hypertrophic cardiomyopathy)http://www.youtube.com/watch?v=QSQx8c8UkUk&feature=fvw
– Abnormal heart (Ruptured papillary muscle)http://www.youtube.com/watch?v=gUdegG0-Shc&feature=related
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Data Preprocessing
Fan Detection– Even images from a single vendor have
different fan areas ATL has four different fan sizes Acuson has different image resolutions etc.
Intensity Normalization– We convert all images to grayscale– Basic linear normalization:
I’ = I / (U – L) Smoothing
– Performed during feature extraction
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Fan Detection
Step One
Step Two
Step Three
Step Four
Step Five
Step Six
Largest connected region approach
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Fan Detection
SuperMask Superimposed on SCR Mask After “AND” operation
Largest connected region approach
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Feature Extraction
Basic Gradients Other Gradient Features Peaks Pixel Intensity Histograms
– Not very useful Statistical Features
– Mean, standard deviation, and statistical moments of pixel intensities in the average frame
Raw Pixel Intensities Alpha Features
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Basic Gradients
Find sum of the magnitudes of the gradients in the x, y, and z directions
These features characterize– Horizontal and vertical structure (x and y gradients)
– Motion (z gradient)
xgrad = ygrad = 0;for each frame { find gradient in x-direction; xsum = sum of magnitudes of all gradients in mask area; xgrad = xgrad + xsum; find gradient in y-direction; ysum = sum of magnitudes of all gradients in mask area; ygrad = ygrad + ysum;}
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Other Gradient Features
XZ and YZ Gradients Real Gradients (x, y, and z) Gradient Sums (x+y, x+z, y+z) Gradient Ratios (x:y, x:z, y:z) Gradient Standard Deviations (x, y, and z)
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Peak Features
Features that characterize the number of horizontal and vertical walls in an image
Potentially useful for distinguishing between apical two-chamber and apical four-chamber views.
Very sensitive to noise
Take average of all frames to produce a single image matrixSum up over all rows of matrixNormalize by the number of fan pixels in each columnSmooth this vector to remove peaks due to noisexpeaks = the number of maxima in the vector