Difference between revisions of "2015 Winter Project Week:PatchRegistration"
From NAMIC Wiki
(Created page with '__NOTOC__ <gallery> Image:PW-MIT2015.png|Projects List Image:WMH_T1.png|Clinical Stroke Image </gallery> ==Key Investigators== - Adrian Dal…') |
|||
| Line 6: | Line 6: | ||
==Key Investigators== | ==Key Investigators== | ||
| − | - Adrian Dalca, | + | - Adrian Dalca, Andreea Bobu, Polina Golland, MIT |
==Project Description== | ==Project Description== | ||
<div style="margin: 20px;"> | <div style="margin: 20px;"> | ||
| − | + | Due to the low quality of clinical images (often with many artifacts, 7mm thick slices, etc), most standard algorithms, such as those for registration, segmentation, and analysis, will fail. | |
| − | + | In part, this is because registration algorithm depend on assumptions of smooth anatomical structures and good quality images, which are not present in these sparse clinical acquisitions. | |
| − | Due to the low quality of clinical images (often with many artifacts, 7mm thick slices, etc), most standard algorithms, such as those for registration, segmentation, analysis, will fail. | + | Here, we are investigating a patch-based discrete image registration which allows for more versatile image metrics and does not impose similar assumptions. |
| − | |||
| − | |||
| − | |||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Objective</h3> | <h3>Objective</h3> | ||
| − | * We will investigate a current | + | * We will investigate a current implementation for patch-based discrete registration on sparse-slice data. |
</div> | </div> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
Latest revision as of 15:38, 7 December 2015
Home < 2015 Winter Project Week:PatchRegistration
Clinical Stroke Image
Key Investigators
- Adrian Dalca, Andreea Bobu, Polina Golland, MIT
Project Description
Due to the low quality of clinical images (often with many artifacts, 7mm thick slices, etc), most standard algorithms, such as those for registration, segmentation, and analysis, will fail. In part, this is because registration algorithm depend on assumptions of smooth anatomical structures and good quality images, which are not present in these sparse clinical acquisitions. Here, we are investigating a patch-based discrete image registration which allows for more versatile image metrics and does not impose similar assumptions.
Objective
- We will investigate a current implementation for patch-based discrete registration on sparse-slice data.
