Name
Title | ||
|---|---|---|
Accurate, fast, and robust vessel contour segmentation of CTA using an adaptive self-learning edge model |
Abstract | ||
|---|---|---|
We present an efficient algorithm for the robust segmentation of vessel contours in Computed Tomography Angiography (CTA) images. The algorithm performs its task within several steps based on a 3D Active Contour Model (ACM) with refinements on Multi-Planar Reconstructions (MPRs) using 2D ACMs. To be able to distinguish true vessel edges from spurious, an adaptive self-learning edge model is applied. We present details of the algorithm together with an evaluation on n=150 CTA data sets and compare the results of the automatic segmentation with manually outlined contours resulting in a median dice similarity coefficient (DSC) of 92.2%. The algorithm is able to render 100 contours within 1.1s on a Pentium®4 CPU 3.20 GHz, 2 GByte of RAM. © 2009 Copyright SPIE - The International Society for Optical Engineering. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1117/12.811364 | Medical Imaging: Image Processing |
Keywords | Field | DocType |
deformable geometry,image-guided therapy/intervention,segmentation,validation,active contour model,computed tomography | Active contour model,Computer vision,Data set,Scale-space segmentation,Computer science,Segmentation,Edge model,Computed tomography,Artificial intelligence,Spurious relationship | Conference |
Volume | Issue | ISSN |
7259 | null | null |
Citations | PageRank | References |
6 | 0.61 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Großkopf | 1 | 14 | 1.87 |
| Christina Biermann | 2 | 9 | 1.16 |
| Kai Deng | 3 | 9 | 2.10 |
| Yan Chen | 4 | 6 | 0.61 |