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

Femando Gasea; Lukas Ramrath

doi:10.1109/IEMBS.2007.4352617

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

Femando Gasea, Lukas Ramrath

Institute of Robotics and Cognitive Systems

Universidad Iberoamericana

Abstract

A method for white matter detection in Optical Coherence Tomography A-Scans is presented. The Kaiman 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.

Original language	English
Title of host publication	2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Number of pages	4
Publisher	IEEE
Publication date	01.12.2007
Pages	1623-1626
Article number	4352617
ISBN (Print)	978-1-4244-0787-3, 978-1-4244-0788-0
DOIs	https://doi.org/10.1109/IEMBS.2007.4352617
Publication status	Published - 01.12.2007
Event	29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society - Lyon, France Duration: 23.08.2007 → 26.08.2007 Conference number: 70818

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Gasea, F., & Ramrath, L. (2007). Model-Based Detection of White Matter in Optical Coherence Tomography Data. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1623-1626). Article 4352617 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings). IEEE. https://doi.org/10.1109/IEMBS.2007.4352617

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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 Kaiman 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.",

author = "Femando Gasea and Lukas Ramrath",

year = "2007",

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doi = "10.1109/IEMBS.2007.4352617",

language = "English",

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Gasea, F & Ramrath, L 2007, Model-Based Detection of White Matter in Optical Coherence Tomography Data. in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society., 4352617, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, IEEE, pp. 1623-1626, 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society , Lyon, France, 23.08.07. https://doi.org/10.1109/IEMBS.2007.4352617

Model-Based Detection of White Matter in Optical Coherence Tomography Data. / Gasea, Femando; Ramrath, Lukas.
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. p. 1623-1626 4352617 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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T1 - Model-Based Detection of White Matter in Optical Coherence Tomography Data

AU - Gasea, Femando

AU - Ramrath, Lukas

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PY - 2007/12/1

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N2 - A method for white matter detection in Optical Coherence Tomography A-Scans is presented. The Kaiman 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.

AB - A method for white matter detection in Optical Coherence Tomography A-Scans is presented. The Kaiman 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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DO - 10.1109/IEMBS.2007.4352617

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SN - 978-1-4244-0788-0

T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

SP - 1623

EP - 1626

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

PB - IEEE

T2 - 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society

Y2 - 23 August 2007 through 26 August 2007

ER -

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

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