Publication: A Fuzzy Logic Technique for Optimizing Follicular Units Measurement of Hair Transplantation
Date
2020
Authors
Mostafa S.A.
Alsobiae A.S.
Ramli A.A.
Mustapha A.
Ali R.R.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
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. � Springer Nature Switzerland AG 2020.
Description
Computer circuits; Data mining; Extraction; Soft computing; Area density; Fuzzy logic system; Fuzzy logic techniques; Fuzzy metrics; Human skin; Medical procedures; Fuzzy logic