Difference between revisions of "2015 Summer Project Week:BigDataFeatures"
From NAMIC Wiki
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<h3>Objective</h3> | <h3>Objective</h3> | ||
* This project will investigate the use of 3D SIFT-RANK image features for organizing and deriving information from 3D medical image volumes. | * 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 domains: chronic obstructive pulmonary disease, Alzheimer's disease, cancer. | ||
</div> | </div> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Approach, Plan</h3> | <h3>Approach, Plan</h3> | ||
| − | * Discussion | + | * Discussion and documentation |
| − | + | - Algorithms: fast KNN methods, hashing, robust estimation (RANSAC, Hough transform). | |
| + | - Mathematical formalisms: probabilistic inference, kernel methods, manifold learning. | ||
</div> | </div> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
Revision as of 17:47, 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 domains: 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.
