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

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.

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

Decision Tree

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

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

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

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

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

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