Blockwise processing applied to brain microvascular network study.
The study of cerebral microvascular networks requires high-resolution images. However, to obtain statistically relevant results, a large area of the brain (several square millimeters) must be analyzed. This leads us to consider huge images, too large to be loaded and processed at once in the memory of a standard computer. To consider a large area, a compact representation of the vessels is required. The medial axis is the preferred tool for this application. To extract it, a dedicated skeletonization algorithm is proposed. Numerous approaches already exist which focus on computational efficiency. However, they all implicitly assume that the image can be completely processed in the computer memory, which is not realistic with the large images considered here. We present in this paper a skeletonization algorithm that processes data locally (in subimages) while preserving global properties (i.e., homotopy). We then show some results obtained on a mosaic of three-dimensional images acquired by confocal microscopy.
Fouard
Céline
C
French National Institute for Research in Computer Science and Control (INRIA), 06902 Sophia Antipolis, France. celine.fouard@imag.fr
Malandain
Grégoire
G
Prohaska
Steffen
S
Westerhoff
Malte
M
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000098:blood supply* / Q000166:cytology*; D002560:Cerebrovascular Circulation; D003205:Computing Methodologies; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008833:Microcirculation / Q000166:cytology*; D018613:Microscopy, Confocal / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity