Name
Abstract | ||
|---|---|---|
We propose an automatic region growing technique for the segmentation of the cerebral cortex and white matter in MRI data. Our method exploits general anatomical knowledge and uses an itera- tive multi resolution scheme for the estimation of intensity distributions to compensate for artifacts within the data. We present a comparison to segmentation results created by the neuroimaging software Brainvoyager QX and show advantages of our approach based on a qualitative and quantitative evaluation. A precise segmentation of the cortical grey and white matter in anatomical MR images is necessary for a large number of medical applications, including morphometry, visualisation and the analysis of the functional organisation of the human brain as assessed by anatomical and functional MRI. An automatic segmentation of the cortex is di-cult because the inter{subject variability of the human brain anatomy restricts the use of anatomical knowledge. Furthermore, image artifacts, such as noise, partial volume efiects and inhomogeneities of the scans, complicate the separation between grey and white matter regions and also the identiflcation of the boundaries of the cortex. Several methods have been applied in recent years to estimate grey and white matter regions on MRI. The most popular methods separate intensity histograms which are assumed to be composed of distributions for the difierent tissue types. The data is then classifled directly(1), or the parameters of the distributions determine the intensity range for region growing approaches (2). Other methods include computationally expensive active contours and surfaces (3, 4, 5). Here, the segmentation of thin gyral folds poses a problem due to partial volume efiects and numerical issues (e.g., related with the curvature{based energy terms). In the presence of magnetic fleld inhomogeneities traditional region growing as well as active contours may underestimate, e.g. the upper part of the frontal lobe(6). Our algorithm resolves the complex task utilising general anatomical knowl- edge. It combines an iterative region growing with fuzzy labels and estimation of the intensity distributions of the grey and white matter using a Gaussian |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-540-93860-6_49 | Bildverarbeitung für die Medizin |
Keywords | Field | DocType |
active contour,partial volume,region growing | Computer vision,White matter,Segmentation,Computer science,Fuzzy logic,Human brain,Software,Artificial intelligence,Region growing,Neuroimaging | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karin Engel | 1 | 31 | 4.88 |
| Frederik Maucksch | 2 | 0 | 0.34 |
| Anja Perlich | 3 | 4 | 1.59 |
| Matthias Wolff | 4 | 68 | 14.17 |
| Klaus Toennies | 5 | 4 | 2.09 |
| André Brechmann | 6 | 108 | 13.43 |