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

2. Course Information Instructor: Dr. Jinbo Bi Office: ITEB 233
Phone:
Web:
Time: Tue / Thu. 3:30-4:45pm
Location: CAST 201
Office hours: Tue. 2:30-3:30pm
HuskyCT
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Illustration

3. 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

4. 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

5. Heart Anatomy

6. Heart Anatomy

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

8. Ultrasound Clips
Parasternal long-axis view, parasternal short-axis view, apical 4-chamber view, apical 2-chamber view
A healthy heart
An abnormal heart (dilated cardiomyopathy)
Abnormal heart (hypertrophic cardiomyopathy)
Abnormal heart (Ruptured papillary muscle)

9. Cardiac ultrasound view separation

10. 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

11. Fan Detection: Different Fan Areas
Large
Regular
Small
Tiny

12. Fan Detection

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

14. Fan Detection
Largest connected region approach

15. Fan Detection
Largest connected region approach

16. 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

17. 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; }

18. Gradient Scatter Plots

19. 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)

20. Gradient Ratio Scatter Plot

21. 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

22. Example Peaks

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

24. Data for Clustering
f f f3
…..
….. ……

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