Difference between revisions of "2015 Summer Project Week:BigDataFeatures"

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* Application domains: registration, segmentation, classification.
 
* Application domains: registration, segmentation, classification.
 
* Image domains: lung CT, brain MR, prostate and brain ultrasound.
 
* Image domains: lung CT, brain MR, prostate and brain ultrasound.
* Clinical domains: chronic obstructive pulmonary disease, Alzheimer's disease, cancer.
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* Clinical use case scenarios: chronic obstructive pulmonary disease, Alzheimer's disease, cancer.
 
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
 
* Discussion and documentation
 
* Discussion and documentation
** Algorithms: fast KNN methods, hashing, robust estimation (RANSAC, Hough transform).
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* Algorithms: fast KNN methods, hashing, robust estimation (RANSAC, Hough transform).
  ** Mathematical formalisms: probabilistic inference, kernel methods, manifold learning.
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* Mathematical formalisms: probabilistic inference, kernel methods, manifold learning.
 
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Revision as of 17:48, 5 June 2015

Home < 2015 Summer Project Week:BigDataFeatures

Key Investigators

  • Matthew Toews, École de Technologie Supérieure
  • William Wells, BWH, Harvard Medical School
  • Tina Kapur, BWH, Harvard Medical School


Project Description

Objective

  • This project will investigate the use of 3D SIFT-RANK image features for organizing and deriving information from 3D medical image volumes.
  • Technology: invariant feature extraction, descriptor representation.
  • Application domains: registration, segmentation, classification.
  • Image domains: lung CT, brain MR, prostate and brain ultrasound.
  • Clinical use case scenarios: chronic obstructive pulmonary disease, Alzheimer's disease, cancer.

Approach, Plan

  • Discussion and documentation
  • Algorithms: fast KNN methods, hashing, robust estimation (RANSAC, Hough transform).
  • Mathematical formalisms: probabilistic inference, kernel methods, manifold learning.

Progress

References