1. Multiple Organ detection in CT Volumes Using Random Forests - Week 9
Daniel Donenfeld

2. Results
Added Non-local patch information to improve classification results
Improved the results minimally due to large amount of background
Added weights to the data in training the random forest
Weight smaller organs more heavily

3. Non-local Patch Information
Generate points surrounding the center of the supervoxel
At each point get mean of a small patch
This encodes information on relative positions of organs
Dang, K., Yuan, J., & Tiong, H. (2013). Voxel labelling in CT images with data-driven contextual features IEEE International Conference on Image Processing.

4. Data Weights Give each data point a weight First assigned class weight
(percent of class volume)-1
Did not improve results
Manually choose weights
Big improvement in results

5. Histogram

6. Gray Level Co-Occurrence Matrix

7. 3D SIFT

8. Haar

9. Future Plans Focus on Haar Features
Best results of the four features
Test different parameters for non-local patch information
Number of patches to sample, radius to sample
Optimize weights on data
Investigate CRF/MRF
Extract bounding boxes