June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Improved visualization of optical coherence tomography (OCT) image using line-divided pattern matching technique
Author Affiliations & Notes
  • Naoki Kobayashi
    Life Science Division, Kowa company, Ltd., Hamamatsu, Japan
  • Toshiaki Nakagawa
    Life Science Division, Kowa company, Ltd., Hamamatsu, Japan
  • Takayoshi Suzuki
    Life Science Division, Kowa company, Ltd., Hamamatsu, Japan
  • Shinji Toyoda
    Life Science Division, Kowa company, Ltd., Hamamatsu, Japan
  • Footnotes
    Commercial Relationships Naoki Kobayashi, Kowa Company, Ltd. (E); Toshiaki Nakagawa, Kowa Company, Ltd. (E); Takayoshi Suzuki, Kowa Company, Ltd. (E); Shinji Toyoda, Kowa Company, Ltd. (E)
  • Footnotes
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Investigative Ophthalmology & Visual Science June 2013, Vol.54, 1515. doi:
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    • Get Citation

      Naoki Kobayashi, Toshiaki Nakagawa, Takayoshi Suzuki, Shinji Toyoda; Improved visualization of optical coherence tomography (OCT) image using line-divided pattern matching technique. Invest. Ophthalmol. Vis. Sci. 2013;54(15):1515.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose
 

To develop a technique based on image registration to correct transverse motion artifacts in OCT images after scan acquisition.

 
Methods
 

The OCT system mainly consists of a commercially available femto-second Ti:Sapphire laser unit (FEMTOLASERS Produktions GmbH, AUSTRIA, Spectral Range : 730 nm - 940 nm) as a light source, a original spectrometer, and a computer for data acquisition and image processing. The OCT system can create cross-sectional images (B-scans) of retinal structure with an axial resolution of 2.0 μm and an imaging speed of 37,000 single axial scans (A-scans) per second. Multiple B-scans through the central fovea were carried out to obtain multiple OCT images. The first stage involved selecting a base image that was suitable for the target image averaging process from all the available images. Moreover, multiple images which have high correlation with the base image were selected. A time averaged images was created within the next stage using a line-divided pattern matching technique from the selected images. The technique comprised the search for a line on the selected image that corresponded to the reference line on the base image. Two lines on the base and selected images, having a similar texture in their respective lines of interest, were regarded as the corresponding lines. The similarity was measured by the cross correlation coefficient.

 
Results
 

Nineteen OCT images were taken from 9 normal subjects. The accuracy of the image registration process can be evaluated by calculating correlation coefficient between the base image and selected images. The correlation coefficient was 0.71±0.04, which was higher than 0.54±0.054 when simple whole-area registration was applied (P<0.0001, paired t-test).

 
Conclusions
 

This technique offers a simple yet effective procedure to significantly improve visualization of OCT image for clinical diagnosis.

 
 
FIGURE. (A) Averaged OCT image based on 30 B-scans after application of the proposed registration technique.
 
FIGURE. (A) Averaged OCT image based on 30 B-scans after application of the proposed registration technique.
 
 
FIGURE. (B) Zoomed-in views of a region indicated in (A).
 
FIGURE. (B) Zoomed-in views of a region indicated in (A).
 
Keywords: 549 image processing • 552 imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound)  
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