This project explores how dictionary methods can be used to describe textures in histology images of mouse brains. We aim to investigate the following problems:
1. Can we use a convolutional neural network (CNN) to learn better texture descriptors ?
2. Can we develop a pursuit method similar to Independent Component Analysis (ICA) that finds the salient textures in the image ?
- Tutorials on boosting: http://www.boosting.org/tutorials
- Boosting Neural Networks:
- Got to http://www.boosting.org/publications and search for "neural networks"
- Googling "boosting sift descriptors" yields:
- Images: /oasis/projects/nsf/csd395/yuncong/CSHL_data_processed/<stack>_lossless_aligned_cropped
Available stacks are MD589, MD592, MD593, MD594, MD595
Each stack has ~130 sections (dimension 15,000 x 10,000 RGB, tif, ~400MB)
- There are two other versions of the same images:
- <stack>_lossless_aligned_cropped_grayscale: images converted to grayscale
- <stack>_lossless_aligned_cropped_downscaled: compressed with lossy jpeg, ~40MB each, for visualization purposes
- Masks: /oasis/projects/nsf/csd395/yuncong/CSHL_data_processed/<stack>_thumbnail_aligned_cropped_mask
contains masks for the thumbnails. To use them on the original-sized images, scale them by 32 on each dimension.