2015 Summer Project Week:BigDataFeatures

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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
  • Utsav Pardasani, Robarts (Observing!)
  • Salvatore Scaramuzzino (Interested!)
  • Andrey Fedorov, BWH

3D SIFT-Rank Visualization, SLC 2015, IPMI 2015

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

Project Description


  • 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.


1) Discussed applications of 3D SIFT-Rank

  • Analyzing COPD - lung CT - Raul San Jose Estepar
  • Identifying similar cases - general MR/CT/US - Tobias Penzkofer
  • Infant brain analysis - brain MR - Steve Pieper
  • Prostate segmentation - MR/US - Andrey Federov, Salvatore Scaramuzzino
  • Astronomical galaxy classification - radio data cube (lambda=21cm) - Davide Punzo

2) Coding for kernel regression framework (C++)


[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.