Difference between revisions of "2016 Winter Project Week/Projects/ImageRestoration"

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Image:PW-MIT2016.png|[[2016_Winter_Project_Week#Projects|Projects List]]
Image:PW-MIT2016.png|[[2016_Winter_Project_Week#Projects|Projects List]]
Image:WMH_T1.png|Clinical Stroke Image
Image:WMH_T1.png|Clinical Stroke Image
Image:gmm_interp.png|Current Result: Original (linear-interpolated) data | our gmm-based interpolation | "true" high resolution data
Image:gmm_interp.png|Running result: Original (linear-interp) data | our gmm-based interp | "true" high res data

Revision as of 18:43, 4 January 2016

Home < 2016 Winter Project Week < Projects < ImageRestoration

Key Investigators

  • Adrian Dalca (MIT)
  • Katie Bouman (MIT)
  • Polina Golland (MIT)

Project Description

Most synthesis, in-painting or super-resolution methods require a training dataset which includes the desired-quality images. Unfortunately, in the clinical setting this is often now available.

Due to the low quality of clinical images (often with many artifacts, 7mm thick slices, etc), most standard algorithms, such as those for registration, segmentation, analysis, will fail.

To improve results for large datasets of clinical-quality data, we are investigating restoration methods without training datasets. Here, we are using a patch-based Gaussian Mixture Model approach with MRF priors and utilizing only the current dataset, without an external training dataset.

Essentially, we explore a model where for a given location in all volumes of a dataset, we model those image patches as drawn from a particular mixture model.

Objective Approach and Plan Progress and Next Steps
  • We will investigate a current model for learning and using Patch-based Gaussian Mixture Model to restore sparse-slice data.
  • We've implemented a small version of the model without regularization and that only works on a small section of a volume. We'll try to deploy it on large-scale dataset on entire volumes.