Feature extraction method for neuro-sensory retinal layer segmentation using statistical estimation in optical coherence tomography

Yeong Mun Cha; Geown Yih; Xuan Gong; Yiyu Chen; Jae Ho Han

doi:10.1109/iww-BCI.2014.6782559

Feature extraction method for neuro-sensory retinal layer segmentation using statistical estimation in optical coherence tomography

Yeong Mun Cha, Geown Yih, Xuan Gong, Yiyu Chen, Jae Ho Han

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We have developed statistical estimation based feature extraction methods for layer segmentation of neuro-sensory retinal images obtained from optical coherence tomography. For clinical diagnosis purposes, a compact functional layer differentiation is targeted in this system so that an upgraded model for the statistical edge detector is considered. Initially, by iteratively searching the maximum edges in regular scopes of A-scans of the image, rough locations of interfaces are found. Then, assigning locational information in sequence to the detected edges, the interfacial locations are accurately detected. The proposed system has been successfully developed for identifying eight retinal layers and the accuracy is much comparable to the commercial equipment. With progressive improvement, we believe that this system will extensively provide the most practical application for various quantitative analyses in clinical diagnoses.

Original language	English
Title of host publication	2014 International Winter Workshop on Brain-Computer Interface, BCI 2014
Publisher	IEEE Computer Society
DOIs	https://doi.org/10.1109/iww-BCI.2014.6782559
Publication status	Published - 2014 Jan 1
Event	2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 - Gangwon, Korea, Republic of Duration: 2014 Feb 17 → 2014 Feb 19

Other

Other	2014 International Winter Workshop on Brain-Computer Interface, BCI 2014
Country/Territory	Korea, Republic of
City	Gangwon
Period	14/2/17 → 14/2/19

Keywords

Feature extraction
image processing
object detection
optical coherence tomography

ASJC Scopus subject areas

Human-Computer Interaction
Human Factors and Ergonomics

Access to Document

10.1109/iww-BCI.2014.6782559

Cite this

Feature extraction method for neuro-sensory retinal layer segmentation using statistical estimation in optical coherence tomography. / Cha, Yeong Mun; Yih, Geown; Gong, Xuan et al.
2014 International Winter Workshop on Brain-Computer Interface, BCI 2014. IEEE Computer Society, 2014. 6782559.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cha, YM, Yih, G, Gong, X, Chen, Y & Han, JH 2014, Feature extraction method for neuro-sensory retinal layer segmentation using statistical estimation in optical coherence tomography. in 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014., 6782559, IEEE Computer Society, 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014, Gangwon, Korea, Republic of, 14/2/17. https://doi.org/10.1109/iww-BCI.2014.6782559

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abstract = "We have developed statistical estimation based feature extraction methods for layer segmentation of neuro-sensory retinal images obtained from optical coherence tomography. For clinical diagnosis purposes, a compact functional layer differentiation is targeted in this system so that an upgraded model for the statistical edge detector is considered. Initially, by iteratively searching the maximum edges in regular scopes of A-scans of the image, rough locations of interfaces are found. Then, assigning locational information in sequence to the detected edges, the interfacial locations are accurately detected. The proposed system has been successfully developed for identifying eight retinal layers and the accuracy is much comparable to the commercial equipment. With progressive improvement, we believe that this system will extensively provide the most practical application for various quantitative analyses in clinical diagnoses.",

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N2 - We have developed statistical estimation based feature extraction methods for layer segmentation of neuro-sensory retinal images obtained from optical coherence tomography. For clinical diagnosis purposes, a compact functional layer differentiation is targeted in this system so that an upgraded model for the statistical edge detector is considered. Initially, by iteratively searching the maximum edges in regular scopes of A-scans of the image, rough locations of interfaces are found. Then, assigning locational information in sequence to the detected edges, the interfacial locations are accurately detected. The proposed system has been successfully developed for identifying eight retinal layers and the accuracy is much comparable to the commercial equipment. With progressive improvement, we believe that this system will extensively provide the most practical application for various quantitative analyses in clinical diagnoses.

AB - We have developed statistical estimation based feature extraction methods for layer segmentation of neuro-sensory retinal images obtained from optical coherence tomography. For clinical diagnosis purposes, a compact functional layer differentiation is targeted in this system so that an upgraded model for the statistical edge detector is considered. Initially, by iteratively searching the maximum edges in regular scopes of A-scans of the image, rough locations of interfaces are found. Then, assigning locational information in sequence to the detected edges, the interfacial locations are accurately detected. The proposed system has been successfully developed for identifying eight retinal layers and the accuracy is much comparable to the commercial equipment. With progressive improvement, we believe that this system will extensively provide the most practical application for various quantitative analyses in clinical diagnoses.

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KW - image processing

KW - object detection

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Feature extraction method for neuro-sensory retinal layer segmentation using statistical estimation in optical coherence tomography

Abstract

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Keywords

ASJC Scopus subject areas

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