Difference between revisions of "Algorithm:MIT:New"

From NAMIC Wiki
Jump to: navigation, search
Line 3: Line 3:
 
= Overview of MIT Algorithms =
 
= Overview of MIT Algorithms =
  
A brief overview of the MIT's algorithms goes here.  This should not be much longer than a paragraph.  Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects.  The projects below are organized into a two column table:  the left column is for representative images and the right column is for project overviews.  The number of rows corresponds to the number of projects at your site.  Put the most interesting and relevant projects at the top of the table.  You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together).
+
A brief overview of the MIT's algorithms goes here.  This should not be much longer than a paragraph.  Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects.  The projects below are organized into a two column table:  the left column is for representative images and the right column is for project overviews.  The number of rows corresponds to the number of projects at your site.  Put the most interesting and relevant projects at the top of the table.  You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together). Testing.
  
 
= MIT Projects =
 
= MIT Projects =

Revision as of 14:36, 10 October 2007

Home < Algorithm:MIT:New

Back to NA-MIC Algorithms

Overview of MIT Algorithms

A brief overview of the MIT's algorithms goes here. This should not be much longer than a paragraph. Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects. The projects below are organized into a two column table: the left column is for representative images and the right column is for project overviews. The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table. You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together). Testing.

MIT Projects

Progress Registration Segmentation Shape.jpg

Shape Based Segmentation and Registration

This type of algorithms assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. More...

New: K.M. Pohl, J. Fisher, S. Bouix, M. Shenton, R. W. McCarley, W.E.L. Grimson, R. Kikinis, and W.M. Wells. Using the Logarithm of Odds to Define a Vector Space on Probabilistic Atlases. Accepted to the Special Issue of Best Selected Papers from MICCAI 06 in Medical Image Analysis More...

Brain.png

Shape Based Level Segmentation

This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. More...

New:

AvgResults.jpg

Registration Regularization

We are interested in the effects of registration regularization on segmentation accuracy in joint registration-segmentation. More...

New: Submission for MICCAI 2007

Wholebrain.jpg

DTI Fiber Clustering and Fiber-Based Analysis

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. More...

New: Monica E. Lemmond, Lauren J. O'Donnell, Stephen Whalen, Alexandra J. Golby. Characterizing Diffusion Along White Matter Tracts Affected by Primary Brain Tumors. Accepted to HBM 2007.

Models.jpg

Fiber Tract Modeling, Clustering, and Quantitative Analysis

The goal of this work is to model the shape of the fiber bundles and use this model discription in clustering and statistical analysis of fiber tracts. More...

New: M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson, Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts,IPMI 2007, Netherlands.

Thalamus algo outline.png

DTI-based Segmentation

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. More...

New: Ulas Ziyan, David Tuch, Carl-Fredrik Westin. Segmentation of Thalamic Nuclei from DTI using Spectral Clustering. Accepted to MICCAI 2006.

FiberBundleReg.jpg

Fiber-Tract-Bundle-based Non-Linear Registration

The goal of this work is to utilize the anatomical information from segmented fiber bundles and use this information for registering fiber tracts and the underlying DTI images. More...

New:

ConnectivityMap.png

Stochastic Tractography

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. More...

New:

FMRIEvaluationchart.jpg

fMRI Detection and Analysis

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. More...

New: Wanmei Ou, Sandy Wells, Polina Golland. Bridging Spatial Regularization And Anatomical Priors in fMRI Detection. In preparation for submission to IEEE TMI.

HippocampalShapeDifferences.gif

Population Analysis of Anatomical Variability

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. More...

New: Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.

SerdarAffineOrig.jpg

Groupwise Registration

We are exploring algorithms for groupwise registration of medical data. More...

New: Serdar K Balci, Polina Golland, Sandy Wells, Lilla Zollei, Mert R Sabuncu and Kinh Tieu. Groupwise registration of medical data.