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Computational BioMedical Informatics

SCE 5095: Special Topics Course

Instructor: Jinbo BiComputer Science and Engineering Dept.

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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

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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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Heart Anatomy

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Heart Anatomy

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Planes of the Heart

Apical 4-chamber

Long-axis view

Short-axis view

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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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Cardiac ultrasound view separation

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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: Different Fan Areas

Large

Regular

Small

Tiny

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Fan Detection

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Fan DetectionStep One

Step Two

Step Three

Step Four

Step Five

Step Six

Largest connected region approach

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Fan Detection

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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Gradient Scatter Plots

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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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Gradient Ratio Scatter Plot

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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

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Example Peaks

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Peak Results

  a2c a4cmin 1 3max 9 6mean 3.72 4.48median 3 4

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Data for Clustering

f1 f2 f3

0.1 1.2 3.4

0.9 3.5 5

……

…..

…..

dn RxxxX },,,{ 21

1x

2x

nx

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Next class

Lab Assignment (no lecture) Classroom changes to

ITEB 138

Instructor and TA available for any questions about Matlab