Difference between revisions of "2015 Summer Project Week:BigDataFeatures"
From NAMIC Wiki
| Line 11: | Line 11: | ||
==Previous Work: 3D SIFT-Rank Visualization, SLC 2015== | ==Previous Work: 3D SIFT-Rank Visualization, SLC 2015== | ||
| − | [[File: | + | [[File:Lung-ct.png|300px|thumb|left|Lung CT Features]] |
| − | [[File:3D_SIFT_Prostate.png|300px|thumb|left|Prostate US]] | + | [[File:3D_SIFT_Prostate.png|300px|thumb|left|Prostate US Features]] |
[[File:Img-feat.png|300px|thumb|left|Data Reduction]] | [[File:Img-feat.png|300px|thumb|left|Data Reduction]] | ||
Revision as of 08:55, 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
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.
