Texture Analysis

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Overview[edit]

MSc Project description

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 ?

Diaries[edit]

References[edit]

Boosting[edit]

Students[edit]

Data[edit]

  • 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.