Name
Abstract | ||
|---|---|---|
Epidemiology studies on vertebra's shape and appearance require big databases of medical images and image processing methods, that are robust against deformation and noise. This work presents a solution of the first step: the vertebrae detection. We propose a method that automatically detects the central spinal curve with 3D data-driven methods in multi-sequence magnetic resonance images (MRI). Additionally, we use simple edge operations for vertebra border detection that can be used for a statistical evaluation with help of some fast user interaction. Our automatic vertebrae detection algorithm fits a polynomial curve through the spinal canal, that afterwards is shifted towards the vertebra centers. An edge operator gives a first approximation of the vertebra borders, that can be evaluated and corrected by some user interaction within 12 seconds. We show, that our algorithm automatically detects more than 90% of all spines correctly, and present a preliminary analysis of vertebrae sizes. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-662-46224-9_3 | BILDVERARBEITUNG FUR DIE MEDIZIN 2015: ALGORITHMEN - SYSTEME - ANWENDUNGEN |
Field | DocType | Citations |
Computer vision,Data-driven,Polynomial,Image processing,Artificial intelligence,Operator (computer programming),Engineering,Vertebra,Intervertebral disc,Spinal canal | Conference | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Kottke | 1 | 0 | 0.34 |
| Gino Gulamhussene | 2 | 0 | 0.34 |
| Klaus D. Tönnies | 3 | 215 | 44.39 |