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 proceedingConference 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 languageEnglish
Title of host publication2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
Pages65-66
Number of pages2
DOIs
Publication statusPublished - 2013 May 17
Event2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 - Gangwon Province, Korea, Republic of
Duration: 2013 Feb 182013 Feb 20

Other

Other2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
CountryKorea, Republic of
CityGangwon Province
Period13/2/1813/2/20

Keywords

  • Feature extraction
  • image processing
  • object detection
  • optical coherence tomography
  • pattern recognition

ASJC Scopus subject areas

  • Human-Computer Interaction

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