title: Model-based detection of white matter in optical coherence tomography data.
abstract: A method for white matter detection in Optical Coherence Tomography A-Scans is presented. The Kalman filter is used to obtain a slope change estimate of the intensity signal. The estimate is subsequently analyzed by a spike detection algorithm and then evaluated by a neural network binary classifier (Perceptron). The capability of the proposed method is shown through the quantitative evaluation of simulated A-Scans. The method was also applied to data obtained from a rat's brain in vitro. Results show that the developed algorithm identifies less false positives than other two spike detection methods, thus, enhancing the robustness and quality of detection.
authors.affiliation: Biomedical Engineering Department, Universidad Iberoamericana, Mexico City, Mexico.
authors.forename: Fernando
authors.initials: F
authors.lastname: Gasca
authors.forename: Lukas
authors.initials: L
authors.lastname: Ramrath
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000166:cytology*; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D041623:Tomography, Optical Coherence / Q000379:methods*