Name
Abstract | ||
|---|---|---|
Many medical imaging techniques commonly use mathematical morphology (MM), with flat discs and spheres as the structuring elements (SE). Many efficient methods have been proposed for various types of SE based on the ability to decompose the SE by way of separability or homotopy. Usually, these methods are only able to approximate disc and sphere SE rather than accomplish MM for the exact SE obtained by discretization of such shapes. We present a method for efficiently computing MM on binary and gray scale image volumes using digitally convex and X-Y-Z symmetric flat SE, which includes discs and spheres. The computational cost is a function of the diameter of the SE and rather than its volume. Additional memory overhead is modest. We are able to compute MM on real medical image volumes with greatly reduced running times and increasing gains for larger SE. Our method is also robust to scale: it is applicable to ellipse and ellipsoid SE which may result from discretizing a disc or sphere on an anisotropic grid. Furthermore, it is easy to implement and can make use of existing image comparison operations. We present performance results on medical chest CT datasets. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1117/12.653982 | Proceedings of SPIE |
Keywords | Field | DocType |
mathematical morphology,structuring element,sphere,disc,circle | Discretization,Ellipsoid,Mathematical optimization,Mathematical morphology,Medical imaging,Algorithm,Structuring element,Ellipse,Mathematics,Grayscale,Grid | Conference |
Volume | ISSN | Citations |
6144 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| michael s vaz | 1 | 0 | 0.34 |
| atilla peter kiraly | 2 | 47 | 9.16 |