전자공학회논문지 (Journal of the Institute of Electronics and Information Engineers)

  • 제53권1호
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  • Pages.118-125
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  • 2016
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  • 2287-5026(pISSN)
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  • 2288-159X(eISSN)
대한전자공학회 (The Institute of Electronics and Information Engineers)
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DOI QR코드

DOI QR Code

스테레오 비전을 이용한 마커리스 정합 : 특징점 추출 방법과 스테레오 비전의 위치에 따른 정합 정확도 평가

Markerless Image-to-Patient Registration Using Stereo Vision : Comparison of Registration Accuracy by Feature Selection Method and Location of Stereo Bision System

  • 주수빈 (성균관대학교 바이오메카트로닉스학과) ;
  • 문정환 (성균관대학교 바이오메카트로닉스학과) ;
  • 신기영 (한국전기연구원)
  • Joo, Subin (Department of Bio-Mechatronic Engineering, Sungkyunkwan University) ;
  • Mun, Joung-Hwan (Department of Bio-Mechatronic Engineering, Sungkyunkwan University) ;
  • Shin, Ki-Young (Korea Electrotechnology Research Institute)
  • 투고 : 2015.09.10
  • 심사 : 2015.12.28
  • 발행 : 2016.01.25
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초록

본 논문에서는 얼굴 영역 수술용 네비게이션을 위한 스테레오 비전과 CT 영상을 이용하여 환자-영상 간 정합(Image to patient registration) 알고리즘의 성능을 평가한다. 환자 영상 간 정합은 스테레오 비전 영상의 특징점 추출과 이를 통한 3차원 좌표 계산, 3차원 좌표와 3차원 CT 영상과의 정합 과정을 거친다. 스테레오 비전 영상에서 3가지 얼굴 특징점 추출 방법과 3가지 정합 방법을 사용하여 생성될 수 있는 5가지 조합 중 정합 정확도가 가장 높은 방법을 평가한다. 또한 머리의 회전에 따라 환자 영상 간 정합의 정확도를 비교한다. 실험을 통해 머리의 회전 각도가 약 20도의 범위 내에서 Active Appearance Model과 Pseudo Inverse Matching을 사용한 정합의 정확도가 가장 높았으며, 각도가 20도 이상일 경우 Speeded Up Robust Features와 Iterative Closest Point를 사용하였을 때 정합 정확도가 높았다. 이 결과를 통해 회전각도가 20도 범위 내에서는 Active Appearance Model과 Pseudo Inverse Matching 방법을 사용하고, 20도 이상의 경우 Speeded Up Robust Features와 Iterative Closest Point를 이용하는 것이 정합의 오차를 줄일 수 있다.

This study evaluates the performance of image to patient registration algorithm by using stereo vision and CT image for facial region surgical navigation. For the process of image to patient registration, feature extraction and 3D coordinate calculation are conducted, and then 3D CT image to 3D coordinate registration is conducted. Of the five combinations that can be generated by using three facial feature extraction methods and three registration methods on stereo vision image, this study evaluates the one with the highest registration accuracy. In addition, image to patient registration accuracy was compared by changing the facial rotation angle. As a result of the experiment, it turned out that when the facial rotation angle is within 20 degrees, registration using Active Appearance Model and Pseudo Inverse Matching has the highest accuracy, and when the facial rotation angle is over 20 degrees, registration using Speeded Up Robust Features and Iterative Closest Point has the highest accuracy. These results indicate that, Active Appearance Model and Pseudo Inverse Matching methods should be used in order to reduce registration error when the facial rotation angle is within 20 degrees, and Speeded Up Robust Features and Iterative Closest Point methods should be used when the facial rotation angle is over 20 degrees.

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