상세정보
학술지
Follicular Unit Classification Method Using Angle Variation of Boundary Vector for Automatic Hair Implant System
- 발행일
- 201602
- 출처
- ETRI Journal, v.38 no.1, pp.195-205
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 협약과제
- 14ZC3100, 지역기반 의료기기.의료로봇 기술개발 및 의료IT 융합 중소기업 활성화 사업, 이형수
- 초록
- This paper presents a novel follicular unit (FU) classification method based on an angle variation of a boundary vector according to the number of hairs in several FU images. The recently developed robotic FU harvest system, ARTAS, classifies through digital imaging the FU type based on the number of hairs with defects in the contour and outline profile of the FU of interest. However, this method has a drawback in that the FU classification is inaccurate because it causes unintended defects in the outline profile of the FU. To overcome this drawback, the proposed method classifies the FU's type by the number of variation points that are calculated using an angle variation a boundary vector. The experimental results show that the proposed method is robust and accurate for various FU shapes, compared to the contour-outline profile FU classification method of the ARTAS system.
- KSP 제안 키워드
- Angle variation, Classification method, Digital imaging(CCD camera), Unit classification, Variation points