Name
Abstract | ||
|---|---|---|
The variability in cortical morphology across subjects makes it difficult to develop a general atlas of cortical sulci. In this paper, we present a data-driven technique for automatically learning cortical folding patterns from MR brain images. A local image feature-based model is learned using machine learning techniques, to describe brain images as a collection of independent, co-occurring, distinct, localized image features which may not be present in all subjects. The choice of feature type (SIFT, KLT, Harris-affine) is explored with regards to identifying cortical folding patterns while also uncovering their group-related variability across subjects. The model is built on lateral volume renderings from the ICBM dataset, and applied to hemisphere classification in order to identify patterns of lateralization based on each feature type. |
Year | Venue | Keywords |
|---|---|---|
2010 | MCV | feature type,local image,group-related variability,brain image,localized image feature,feature-based model,mr brain image,cortical folding pattern variability,cortical sulcus,local image feature,cortical morphology,cortical folding pattern,machine learning,volume rendering,brain imaging,image features |
Field | DocType | Volume |
Scale-invariant feature transform,Computer vision,Lateralization of brain function,Pattern recognition,Computer science,Feature (computer vision),Artificial intelligence,Rendering (computer graphics) | Conference | 6533 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rishi Rajalingham | 1 | 13 | 1.58 |
| Matthew Toews | 2 | 247 | 20.60 |
| Louis Collins | 3 | 69 | 5.53 |
| Tal Arbel | 4 | 942 | 63.08 |