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

Institut für Robotik und Kognitive Systeme

Universidad Iberoamericana

1 Zitat (Scopus)

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.

Originalsprache	Englisch
Titel	2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Seitenumfang	4
Herausgeber (Verlag)	IEEE
Erscheinungsdatum	01.12.2007
Seiten	1623-1626
Aufsatznummer	4352617
ISBN (Print)	978-1-4244-0787-3, 978-1-4244-0788-0
DOIs	https://doi.org/10.1109/IEMBS.2007.4352617
Publikationsstatus	Veröffentlicht - 01.12.2007
Veranstaltung	29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society - Lyon, Frankreich Dauer: 23.08.2007 → 26.08.2007 Konferenznummer: 70818

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10.1109/IEMBS.2007.4352617

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https://www.scopus.com/pages/publications/57649213558

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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 (S. 1623-1626). Artikel 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",

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doi = "10.1109/IEMBS.2007.4352617",

language = "English",

isbn = "978-1-4244-0787-3",

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pages = "1623--1626",

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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, S. 1623-1626, 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society , Lyon, Frankreich, 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. S. 1623-1626 4352617 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

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

AU - Gasea, Femando

AU - Ramrath, Lukas

N1 - Conference code: 70818

PY - 2007/12/1

Y1 - 2007/12/1

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.

UR - https://www.scopus.com/pages/publications/57649213558

U2 - 10.1109/IEMBS.2007.4352617

DO - 10.1109/IEMBS.2007.4352617

M3 - Conference contribution

SN - 978-1-4244-0787-3

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

Abstract

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