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Model-Based Detection of White Matter in Optical Coherence Tomography Data

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2 Author(s)
Fernando Gasca ; Biomedical Engineering Department, Universidad Iberoamericana, Mexico City, Mexico ; Lukas Ramrath

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.

Published in:

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

22-26 Aug. 2007