CT‐based sex estimation on human femora using statistical shape modeling. Issue 2 (30th March 2019)
- Record Type:
- Journal Article
- Title:
- CT‐based sex estimation on human femora using statistical shape modeling. Issue 2 (30th March 2019)
- Main Title:
- CT‐based sex estimation on human femora using statistical shape modeling
- Authors:
- Fliss, Barbara
Luethi, Marcel
Fuernstahl, Philipp
Christensen, Angi M.
Sibold, Ken
Thali, Michael
Ebert, Lars C. - Abstract:
- Abstract: Objectives: Estimating the sex of decomposed corpses and skeletal remains of unknown individuals is one of the first steps in the identification process in forensic contexts. Although various studies have considered the femur for sex estimation, the focus has primarily been on a specific single or a handful of measurements rather than the entire shape of the bone. In this article, we use statistical shape modeling (SSM) for sex estimation. We hypothesize that the accuracy of sex estimation will be improved by using the entire shape. Materials and Methods: For this study, we acquired a total of 61 femora from routine postmortem CT scans at the Institute for Forensic Medicine of the University of Zurich. The femora were extracted using segmentation technique. After building a SSM, we used the linear regression and nonlinear support vector machine technique for classification. Results: Using linear logistic regression and only the first principal component of the SSM, 76% of the femora were correctly classified by sex. Using the first five principal components, this value could be increased to 80%. Using nonlinear support vector machines and the first 20 principal components increased the rate of correctly classified femora to 87%. Discussion: Despite some limitations, the results obtained by using SSM for sex estimation in femur were promising and confirm the findings of other studies. Sex estimation accuracy, however, is not significantly improved over single orAbstract: Objectives: Estimating the sex of decomposed corpses and skeletal remains of unknown individuals is one of the first steps in the identification process in forensic contexts. Although various studies have considered the femur for sex estimation, the focus has primarily been on a specific single or a handful of measurements rather than the entire shape of the bone. In this article, we use statistical shape modeling (SSM) for sex estimation. We hypothesize that the accuracy of sex estimation will be improved by using the entire shape. Materials and Methods: For this study, we acquired a total of 61 femora from routine postmortem CT scans at the Institute for Forensic Medicine of the University of Zurich. The femora were extracted using segmentation technique. After building a SSM, we used the linear regression and nonlinear support vector machine technique for classification. Results: Using linear logistic regression and only the first principal component of the SSM, 76% of the femora were correctly classified by sex. Using the first five principal components, this value could be increased to 80%. Using nonlinear support vector machines and the first 20 principal components increased the rate of correctly classified femora to 87%. Discussion: Despite some limitations, the results obtained by using SSM for sex estimation in femur were promising and confirm the findings of other studies. Sex estimation accuracy, however, is not significantly improved over single or multiple linear measurements. Further research might improve the sex determination process in forensic anthropology by using SSM. … (more)
- Is Part Of:
- American journal of physical anthropology. Volume 169:Issue 2(2019)
- Journal:
- American journal of physical anthropology
- Issue:
- Volume 169:Issue 2(2019)
- Issue Display:
- Volume 169, Issue 2 (2019)
- Year:
- 2019
- Volume:
- 169
- Issue:
- 2
- Issue Sort Value:
- 2019-0169-0002-0000
- Page Start:
- 279
- Page End:
- 286
- Publication Date:
- 2019-03-30
- Subjects:
- CT -- forensic anthropology -- forensic imaging -- sex estimation -- statistical shape modeling
Physical anthropology -- Periodicals
Anthropology -- Periodicals
Anthropologie physique -- Périodiques
599.9 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/ajpa.23828 ↗
- Languages:
- English
- ISSNs:
- 0002-9483
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0832.000000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 10209.xml