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  • Title: Model-based detection of white matter in optical coherence tomography data.
    Author: Gasca F, Ramrath L.
    Journal: Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():1623-6. PubMed ID: 18002283.
    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.
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