Compact Representations for Fast Nonrigid Registration of Medical Images
We develop efficient techniques for the non-rigid registration of medical images by using representations that adapt to the anatomy found in such images. Images of anatomical structures typically have uniform intensity interiors and smooth boundaries. We create methods to represent such regions co...
| Main Author: | |
|---|---|
| Language: | en_US |
| Published: |
2005
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/29830 |
| _version_ | 1826191848815722496 |
|---|---|
| author | Timoner, Samson |
| author_facet | Timoner, Samson |
| author_sort | Timoner, Samson |
| collection | MIT |
| description | We develop efficient techniques for the non-rigid registration of medical
images by using representations that adapt to the anatomy found in such
images.
Images of anatomical structures typically have uniform intensity interiors
and smooth boundaries. We create methods to represent such regions
compactly using tetrahedra. Unlike voxel-based representations, tetrahedra
can accurately describe the expected smooth surfaces of medical
objects. Furthermore, the interior of such objects can be represented using
a small number of tetrahedra. Rather than describing a medical object using
tens of thousands of voxels, our representations generally contain only a few
thousand elements.
Tetrahedra facilitate the creation of efficient non-rigid registration
algorithms based on finite element methods (FEM). We create a fast,
FEM-based method to non-rigidly register segmented anatomical structures
from two subjects. Using our compact tetrahedral representations, this
method generally requires less than one minute of processing time on a desktop
PC.
We also create a novel method for the non-rigid registration of gray scale
images. To facilitate a fast method, we create a tetrahedral representation
of a displacement field that automatically adapts to both the anatomy in an
image and to the displacement field. The resulting algorithm has a
computational cost that is dominated by the number of nodes in the mesh
(about 10,000), rather than the number of voxels in an image (nearly
10,000,000). For many non-rigid registration problems, we can find a
transformation from one image to another in five minutes. This speed is
important as it allows use of the algorithm during surgery.
We apply our algorithms to find correlations between the shape of
anatomical structures and the presence of schizophrenia. We show that a
study based on our representations outperforms studies based on other
representations. We also use the results of our non-rigid registration
algorithm as the basis of a segmentation algorithm. That algorithm also
outperforms other methods in our tests, producing smoother segmentations
and more accurately reproducing manual segmentations. |
| first_indexed | 2024-09-23T09:02:13Z |
| id | mit-1721.1/29830 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:02:13Z |
| publishDate | 2005 |
| record_format | dspace |
| spelling | mit-1721.1/298302019-04-10T20:22:26Z Compact Representations for Fast Nonrigid Registration of Medical Images Timoner, Samson AI non-rigid registration medical image processing We develop efficient techniques for the non-rigid registration of medical images by using representations that adapt to the anatomy found in such images. Images of anatomical structures typically have uniform intensity interiors and smooth boundaries. We create methods to represent such regions compactly using tetrahedra. Unlike voxel-based representations, tetrahedra can accurately describe the expected smooth surfaces of medical objects. Furthermore, the interior of such objects can be represented using a small number of tetrahedra. Rather than describing a medical object using tens of thousands of voxels, our representations generally contain only a few thousand elements. Tetrahedra facilitate the creation of efficient non-rigid registration algorithms based on finite element methods (FEM). We create a fast, FEM-based method to non-rigidly register segmented anatomical structures from two subjects. Using our compact tetrahedral representations, this method generally requires less than one minute of processing time on a desktop PC. We also create a novel method for the non-rigid registration of gray scale images. To facilitate a fast method, we create a tetrahedral representation of a displacement field that automatically adapts to both the anatomy in an image and to the displacement field. The resulting algorithm has a computational cost that is dominated by the number of nodes in the mesh (about 10,000), rather than the number of voxels in an image (nearly 10,000,000). For many non-rigid registration problems, we can find a transformation from one image to another in five minutes. This speed is important as it allows use of the algorithm during surgery. We apply our algorithms to find correlations between the shape of anatomical structures and the presence of schizophrenia. We show that a study based on our representations outperforms studies based on other representations. We also use the results of our non-rigid registration algorithm as the basis of a segmentation algorithm. That algorithm also outperforms other methods in our tests, producing smoother segmentations and more accurately reproducing manual segmentations. 2005-12-12T23:24:20Z 2005-12-12T23:24:20Z 2003-07-04 MIT-CSAIL-TR-2003-001 AITR-2003-015 http://hdl.handle.net/1721.1/29830 en_US Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory 183 p. 160218641 bytes 8166856 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | AI non-rigid registration medical image processing Timoner, Samson Compact Representations for Fast Nonrigid Registration of Medical Images |
| title | Compact Representations for Fast Nonrigid Registration of Medical Images |
| title_full | Compact Representations for Fast Nonrigid Registration of Medical Images |
| title_fullStr | Compact Representations for Fast Nonrigid Registration of Medical Images |
| title_full_unstemmed | Compact Representations for Fast Nonrigid Registration of Medical Images |
| title_short | Compact Representations for Fast Nonrigid Registration of Medical Images |
| title_sort | compact representations for fast nonrigid registration of medical images |
| topic | AI non-rigid registration medical image processing |
| url | http://hdl.handle.net/1721.1/29830 |
| work_keys_str_mv | AT timonersamson compactrepresentationsforfastnonrigidregistrationofmedicalimages |