1. Multiple Organ detection in CT Volumes Using Random Forests - Week 5
Daniel Donenfeld

2. Work This week Test handcrafted features for classifying supervoxels
Patch Features
Position and Histogram Features
Features at a Point
SIFT3D1
HOG3D2
Paul Scovanner, Saad Ali, and Mubarak Shah, A 3-Dimensional SIFT Descriptor and its Application to Action Recognition, ACM MM 2007.
Klaeser, A., Marszalek, M., & Schmid, C. (n.d.). A Spatio-Temporal Descriptor Based on 3D-Gradients. Procedings of the British Machine Vision Conference 2008.

3. Decision Tree Decision Trees
Start at root, and go to child depending on decision rule
EX: if sunny then go to left node, otherwise go to right node
Leaves have labels for the data
To classify, start at root and apply decisions to data until reach the leaves

4. Decision Tree

5. Random Forest Decision Trees - Prone to overfitting
Random Forest - Create multiple decision trees from a subset of the data, using a random subset of features
variance of each classifier reduces overfitting
At each leaf node is a histogram of probabilities for each class

6. Decision Tree
Data
Send Data to multiple Decision trees and average the resulting histograms

7. Results Num Trees # of Training # of Testing Number Correct
Percent Correct
Features
Notes
100
3000
2589
0.863
Center, Histogram - Mean, Variance, Max, Min
Training set is person 1
Testing set is person 11
1000
8999
2573
0.857
Training set is people 1,2,3
2585
0.861
Center, Histogram - Mean, Variance, Max, Min, 3D SIFT
Test is person 11
2584
Training Set is people 1,2,3
2646
0.882
Center, Histogram - Mean, Variance, Max,
Min, 3D HOG
Training Set is person 2, Test is person 3

8. Result No appreciable difference in methods, HOG is marginally better
More Data is needed to get a fuller view of performance
Percent Correct

9. Number of Trees
Train and test a random forest with a different number of trees (from 1 to 150)
Use the same set of training and testing data
With a small number - worse results
Using multiple classifiers increases the performance
Number Correct
Number Trees

10. Next Week Plan Still running code on complete dataset
Supervoxel code takes ~8 hours/patient
Continue testing 3DSIFT and 3DHOG
Test other 3D image features
3D gabor filters for texture information
Improve classification using distance priors