Name
Abstract | ||
|---|---|---|
We present the results of constructing a probabilistic volumetric model of 3D MR kidney images. The ultimate goal of this work is the mouse kidney segmentation based on a probabilistic volumetric model. The kidneys were aligned into the base shape using an extended robust point matching algorithm. The registration step consists of the global linear transformation and the local B-spline based free form deformation. Shape modeling is performed with globally aligned shape and template volumetric image is generated with locally aligned images. We are currently working on developing a segmentation algorithm using our model. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30136-3_134 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
linear transformation,probabilistic model,local alignment | Computer vision,Point set registration,Mouse Kidney,Pattern recognition,Computer science,Segmentation,Free-form deformation,Volumetric model,Artificial intelligence,Statistical model,Linear map,Probabilistic logic | Conference |
Volume | ISSN | Citations |
3217 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hirohito Okuda | 1 | 22 | 1.71 |
| Pavel Shkarin | 2 | 8 | 1.71 |
| Kevin L Behar | 3 | 12 | 2.59 |
| James S. Duncan | 4 | 2973 | 466.48 |
| Xenophon Papademetris | 5 | 516 | 48.77 |