Separation of arteries and veins in the cerebral cortex using physiological oscillations by optical imaging of intrinsic signal.
An automated method is presented for artery-vein separation in cerebral cortical images recorded with optical imaging of the intrinsic signal. The vessel-type separation method is based on the fact that the spectral distribution of intrinsic physiological oscillations varies from arterial regions to venous regions. In arterial regions, the spectral power is higher in the heartbeat frequency (HF), whereas in venous regions, the spectral power is higher in the respiration frequency (RF). The separation method was begun by extracting the vascular network and its centerline. Then the spectra of the optical intrinsic signals were estimated by the multitaper method. A standard F-test was performed on each discrete frequency point to test the statistical significance at the given level. Four periodic physiological oscillations were examined: HF, RF, and two other eigenfrequencies termed F1 and F2. The separation of arteries and veins was implemented with the fuzzy c-means clustering method and the region-growing approach by utilizing the spectral amplitudes and power-ratio values of the four eigenfrequencies on the vasculature. Subsequently, independent spectral distributions in the arteries, veins, and capillary bed were estimated for comparison, which showed that the spectral distributions of the intrinsic signals were very distinct between the arterial and venous regions.
Hu
Dewen
D
National University of Defense Technology, College of Mechatronics and Automation, Department of Automatic Control, Changsha, Hunan 410073, China. dwhu@nudt.edu.cn
Wang
Yucheng
Y
Liu
Yadong
Y
Li
Ming
M
Liu
Fayi
F
D000465:Algorithms; D000818:Animals; D001158:Arteries / Q000033:anatomy & histology; D002540:Cerebral Cortex / Q000098:blood supply*; D002560:Cerebrovascular Circulation / Q000502:physiology*; D016000:Cluster Analysis; D003952:Diagnostic Imaging / Q000295:instrumentation / Q000379:methods*; D005654:Fundus Oculi; D017143:Fuzzy Logic; D007091:Image Processing, Computer-Assisted; D008297:Male; D055095:Optics and Photonics / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated; D051381:Rats; D017207:Rats, Sprague-Dawley; D014680:Veins / Q000033:anatomy & histology