Name
Abstract | ||
|---|---|---|
This paper presents a new method of constructing compact statistical point-based models of populations of human cortical surfaces with functions of spatial locations driving the correspondence optimization. The proposed method is to establish a tradeoff between an even sampling of the surfaces (a low surface entropy) and the similarity of corresponding points across the population (a low ensemble entropy). The similarity metric, however, isn't constrained to be just spatial proximity, but can be any function of spatial location, thus allowing the integration of local cortical geometry as well as DTI connectivity maps and vasculature information from MRA images. This method does not require a spherical parameterization or fine tuning of parameters. Experimental results are also presented, showing lower local variability for both sulcal depth and cortical thickness measurements, compared to other commonly used methods such as FreeSurfer. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISBI.2008.4541327 | 2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4 |
Keywords | Field | DocType |
correspondence, image shape analysis, brain modeling, statistics, image registration | Population,Computer vision,Particle system,Pattern recognition,Parametrization,Computer science,Fine-tuning,Artificial intelligence,Sampling (statistics),Image registration,Image sampling,Statistical analysis | Conference |
ISSN | Citations | PageRank |
1945-7928 | 15 | 0.89 |
References | Authors | |
7 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ipek Oguz | 1 | 108 | 12.87 |
| Joshua E. Cates | 2 | 209 | 17.05 |
| P Thomas Fletcher | 3 | 779 | 51.97 |
| Ross T. Whitaker | 4 | 108 | 7.15 |
| Derek Cool | 5 | 49 | 5.69 |
| Stephen R Aylward | 6 | 608 | 61.21 |
| Martin Styner | 7 | 1349 | 116.30 |