- Volume 38 Issue 1
DOI QR Code
Follicular Unit Classification Method Using Angle Variation of Boundary Vector for Automatic Hair Implant System
- Kim, Hwi Gang (IT Convergence Research Laboratory, ETRI) ;
- Bae, Tae Wuk (IT Convergence Research Laboratory, ETRI) ;
- Kim, Kyu Hyung (IT Convergence Research Laboratory, ETRI) ;
- Lee, Hyung Soo (IT Convergence Research Laboratory, ETRI) ;
- Lee, Soo In (Broadcasting & Telecommunications Media Research Laboratory, ETRI)
- Received : 2014.08.18
- Accepted : 2015.07.22
- Published : 2016.02.01
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.
Grant : Local-based medical device/robot development & medical IT convergence for small and medium enterprise revitalization project
Supported by : ETRI
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