Difference between revisions of "Projects:MultimodalAtlas"
(New page: Back to NA-MIC_Collaborations, MIT Algorithms In many medical image analysis applications we have a large collection of data sets available to ...) |
|||
| Line 1: | Line 1: | ||
Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]], [[Algorithm:MIT|MIT Algorithms]] | Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]], [[Algorithm:MIT|MIT Algorithms]] | ||
| − | In many medical image analysis applications we have a large collection of data sets available to us. In such applications, we are faced with at least two different challenges: first, most analyses we are interested in carrying out on the data-sets require point-wise spatial correspondence across the images. Co-registration of the image data set directly or indirectly attempts to solve this problem by spatially warping the images into a common, so-called “atlas space.” Another important challenge we face is coming up with a summary of the collection – this summary is usually called a template or an atlas, which is usually an average image. | + | In many medical image analysis applications we have a large collection of data sets available to us. In such applications, we are faced with at least two different challenges: first, most analyses we are interested in carrying out on the data-sets require point-wise spatial correspondence across the images. Co-registration of the image data set directly or indirectly attempts to solve this problem by spatially warping the images into a common, so-called “atlas space.” Another important challenge we face is coming up with a summary of the collection – this summary is usually called a template or an atlas, which is usually an average image. The famous MNI template brain, commonly used in neuroimaging studies is one example. Templates can be used to “normalize” new images, that is bring them into the common atlas space: for localizing regions of interests in the new subject or comparing groups of interest. |
| + | |||
| + | Recent work has acknowledged the fact that in some cases a single template may not suffice to summarize the whole population. For example, in Blezek and Miller MICCAI '06, the authors propose an atlas stratification algorithm that computes multiple templates. In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''' for Image Clustering, is based on the following idea: given the templates, the co-registration problem becomes simple, reducing to a number of pairwise registration instances. On the other hand, if we are given a collection of images that have been co-registered, i.e. in perfect alignment, then an off-the shelf clustering or averaging algorithm can be used to compute the templates. | ||
| + | |||
| + | = Description = | ||
| + | |||
| + | [Image:iCluster_GM.gif|thumb|center|300px|Generative Model used in iCluster.]] | ||
Revision as of 19:35, 9 November 2007
Home < Projects:MultimodalAtlasBack to NA-MIC_Collaborations, MIT Algorithms
In many medical image analysis applications we have a large collection of data sets available to us. In such applications, we are faced with at least two different challenges: first, most analyses we are interested in carrying out on the data-sets require point-wise spatial correspondence across the images. Co-registration of the image data set directly or indirectly attempts to solve this problem by spatially warping the images into a common, so-called “atlas space.” Another important challenge we face is coming up with a summary of the collection – this summary is usually called a template or an atlas, which is usually an average image. The famous MNI template brain, commonly used in neuroimaging studies is one example. Templates can be used to “normalize” new images, that is bring them into the common atlas space: for localizing regions of interests in the new subject or comparing groups of interest.
Recent work has acknowledged the fact that in some cases a single template may not suffice to summarize the whole population. For example, in Blezek and Miller MICCAI '06, the authors propose an atlas stratification algorithm that computes multiple templates. In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called iCluster for Image Clustering, is based on the following idea: given the templates, the co-registration problem becomes simple, reducing to a number of pairwise registration instances. On the other hand, if we are given a collection of images that have been co-registered, i.e. in perfect alignment, then an off-the shelf clustering or averaging algorithm can be used to compute the templates.
Description
[Image:iCluster_GM.gif|thumb|center|300px|Generative Model used in iCluster.]]
