Efficient edge detection method for anatomic feature extraction of neuro-sensory tissue image based on optical coherence tomography

Yeong Mun Cha; Jae Ho Han

doi:10.1109/IWW-BCI.2013.6506632

Efficient edge detection method for anatomic feature extraction of neuro-sensory tissue image based on optical coherence tomography

Yeong Mun Cha, Jae Ho Han

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

2 Citations (Scopus)

Abstract

In this work, we propose a reliable and detailed edge detection method customized on characteristics of optical coherence tomography images for stable feature extraction. Using a local window holding many pixels for tracking structural tendencies, edges are detected on reliably limited areas in reduced noise effect. For detailed pixel separation between structures, the edge detection is also achieved through clustering based on Gaussian mixture model. As results, the detected edges showed less than 3-m of average distant differences compared to edges on manually recognized images. We believe this feature extraction method will provide improved quantitative analyses in wide OCT research areas.

Original language	English
Title of host publication	2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
Pages	65-66
Number of pages	2
DOIs	https://doi.org/10.1109/IWW-BCI.2013.6506632
Publication status	Published - 2013
Event	2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 - Gangwon Province, Korea, Republic of Duration: 2013 Feb 18 → 2013 Feb 20

Publication series

Name	2013 International Winter Workshop on Brain-Computer Interface, BCI 2013

Other

Other	2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
Country/Territory	Korea, Republic of
City	Gangwon Province
Period	13/2/18 → 13/2/20

Keywords

Feature extraction
image processing
object detection
optical coherence tomography
pattern recognition

ASJC Scopus subject areas

Human-Computer Interaction

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10.1109/IWW-BCI.2013.6506632

Cite this

Cha, Y. M., & Han, J. H. (2013). Efficient edge detection method for anatomic feature extraction of neuro-sensory tissue image based on optical coherence tomography. In 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 (pp. 65-66). Article 6506632 (2013 International Winter Workshop on Brain-Computer Interface, BCI 2013). https://doi.org/10.1109/IWW-BCI.2013.6506632

Efficient edge detection method for anatomic feature extraction of neuro-sensory tissue image based on optical coherence tomography. / Cha, Yeong Mun; Han, Jae Ho.
2013 International Winter Workshop on Brain-Computer Interface, BCI 2013. 2013. p. 65-66 6506632 (2013 International Winter Workshop on Brain-Computer Interface, BCI 2013).

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

Cha, YM & Han, JH 2013, Efficient edge detection method for anatomic feature extraction of neuro-sensory tissue image based on optical coherence tomography. in 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013., 6506632, 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013, pp. 65-66, 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013, Gangwon Province, Korea, Republic of, 13/2/18. https://doi.org/10.1109/IWW-BCI.2013.6506632

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AB - In this work, we propose a reliable and detailed edge detection method customized on characteristics of optical coherence tomography images for stable feature extraction. Using a local window holding many pixels for tracking structural tendencies, edges are detected on reliably limited areas in reduced noise effect. For detailed pixel separation between structures, the edge detection is also achieved through clustering based on Gaussian mixture model. As results, the detected edges showed less than 3-m of average distant differences compared to edges on manually recognized images. We believe this feature extraction method will provide improved quantitative analyses in wide OCT research areas.

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Efficient edge detection method for anatomic feature extraction of neuro-sensory tissue image based on optical coherence tomography

Abstract

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Keywords

ASJC Scopus subject areas

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