Computational BioMedical Informatics SCE 5095: Special Topics Course Instructor: Jinbo Bi Computer Science and Engineering Dept.

Course Information Instructor: Dr. Jinbo Bi Office: ITEB 233 Phone: 860-486-1458 Email: jinbo@engr.uconn.edu 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

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

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

Heart Anatomy

Heart Anatomy

Planes of the Heart Short-axis view Long-axis view Apical 4-chamber

Ultrasound Clips Parasternal long-axis view, parasternal short-axis view, apical 4-chamber view, apical 2-chamber view A healthy heart http://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

Cardiac ultrasound view separation

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

Fan Detection: Different Fan Areas Large Regular Small Tiny

Fan Detection

Fan Detection Largest connected region approach Step One Step Two Step Three Step Four Step Five Step Six

Fan Detection Largest connected region approach

Fan Detection Largest connected region approach

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

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; }

Gradient Scatter Plots

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)

Gradient Ratio Scatter Plot

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 matrix Sum up over all rows of matrix Normalize by the number of fan pixels in each column Smooth this vector to remove peaks due to noise xpeaks = the number of maxima in the vector

Example Peaks

Peak Results   a2c a4c min 1 3 max 9 6 mean 3.72 4.48 median 4

Data for Clustering f1 f2 f3 0.1 1.2 3.4 0.9 3.5 5 ….. ….. ……

Next class ITEB 138 Lab Assignment (no lecture) Classroom changes to Instructor and TA available for any questions about Matlab