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A Fuzzy Logic Technique for Optimizing Follicular Units Measurement of Hair Transplantation

  • Salama A. MostafaEmail author
  • Abdullah S. Alsobiae
  • Azizul Azhar Ramli
  • Aida Mustapha
  • Rabei Raad Ali
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 978)

Abstract

Hair transplantation medical procedure is one of the main methods that are at present utilized in the treatment of balding of the scalp. It is essentially a procedure of extricating or taking a particular number of Follicular Units (FUs) from the back of the head which serves as the contributor or donor region and transplanting them in the region of the scalp that is going bald. A FU comprises one to five normally occurring human skin hairs. The most mainstream techniques designed for hair transplantation dependent on the FUs idea is the Follicular Units Extraction (FUE). Past endeavors to calculate the needed number of FUs for the FUE failed to put into consideration various metrics or indices (parameters) associated with the determination procedure. This paper expounds a Fuzzy Logic Follicular Units Measurement (FL-FUM) strategy for hair transplantation of the FUT and FUE techniques. The FL-FUM technique gives a progressively exact estimation of the needed FUs number by envisaging about three fuzzy metrics of Age, Race and Donor Area Density (DAD). Its objective is to help hair reclamation people who utilize the FUT and FUE techniques in assessing the needed number of necessary grafts that fulfill a patient’s baldness state. The FL-FUM strategy employs a Fuzzy Logic system on the three metrics (fuzzy sets) to defuzzify the assessment of the FUs dependent on Visualized Male Pattern Baldness Schema. The strategy is tried and assessed by contrasting its outcomes and the comparable existing strategies and is observed to be highly productive for real estimation cases.

Keywords

Hair transplantation Follicular units transplantation Follicular units extraction Follicular units measurement Fuzzy logic 

Notes

Acknowledgements

This work is sponsored by Universiti Tun Hussein Onn Malaysia under TIER1 FASA 1/2007, UTHM Research Grant (VOT U896) and Gates IT Sdn. Bhd.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Salama A. Mostafa
    • 1
    Email author
  • Abdullah S. Alsobiae
    • 2
  • Azizul Azhar Ramli
    • 1
  • Aida Mustapha
    • 1
  • Rabei Raad Ali
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaBatu PahatMalaysia
  2. 2.College of Computing and InformaticsUniversiti Tenaga NasionalKajangMalaysia

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