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

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[[File:Lung-ct.png|300px|thumb|left|Lung CT Features]]
 
[[File:Lung-ct.png|300px|thumb|left|Lung CT Features]]
 
[[File:3D_SIFT_Prostate.png|300px|thumb|left|Prostate US Features]]
 
[[File:3D_SIFT_Prostate.png|300px|thumb|left|Prostate US Features]]
[[File:Img-feat.png‎|300px|thumb|left|Data Reduction]]
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[[File:Img-feat.png‎|300px|thumb|left|Data Reduction for 20000 lung CT volumes]]
  
 
==Project Description==
 
==Project Description==

Revision as of 09:19, 22 June 2015

Home < 2015 Summer Project Week:BigDataFeatures

Key Investigators

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

Previous Work: 3D SIFT-Rank Visualization, SLC 2015

Lung CT Features
Prostate US Features
Data Reduction for 20000 lung CT volumes

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

[1] SIFT View, NAMIC 2015 SLC Project Week
[2] "A Feature-based Approach to Big Data Analysis of Medical Images", M. Toews, C. Wachinger, R. S. J. Estepar, W.M. Wells III. Information Processing in Medical Imaging (IPMI), 2015.
[3] "Keypoint Transfer Segmentation", C. Wachinger, M. Toews, G. Langs, W.M. Wells IIIi, P. Golland. Information Processing in Medical Imaging (IPMI), 2015.