The understanding of locomotor patterns, activity schemes, and biological variations has been enhanced by the study of the geometrical properties and cortical bone thickness of the long bones measured using CT scan cross-sections. With the development of scanning procedures, the internal architecture of the long bones can be explored along the entire diaphysis. Recently, several methods that map cortical thickness along the whole femoral diaphysis have been developed. Precise homology is vital for statistical examination of the data; however, the repeatability of these methods is unknown and some do not account for the curvature of the bones. We have designed a semiautomatic workflow that improves the morphometric analysis of cortical thickness, including robust data acquisition with minimal user interaction and considering the bone curvature. The proposed algorithm also performs automatic landmark refinement and rigid registration on the extracted morphometric maps of the cortical thickness. Because our algorithm automatically reslices the diaphysis into 100 cross-sections along the medial axis and uses an adaptive thresholding method, it is usable on CT scans that contain soft tissues as well as on bones that have not been oriented specifically prior to scanning. Our approach exhibits considerable robustness to error in user-supplied landmarks, suppresses distortion caused by the curvature of the bones, and calculates the curvature of the medial axis.
- MeSH
- algoritmy MeSH
- antropologie fyzická MeSH
- femur diagnostické zobrazování MeSH
- lidé MeSH
- počítačová rentgenová tomografie metody MeSH
- počítačové zpracování obrazu metody MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The greater sciatic notch (GSN) is one of the most important and frequently used characteristics for determining the sex of skeletons, but objective assessment of this characteristic is not without its difficulties. We tested the robustness of GSN sex classification on the basis of geometric morphometrics (GM) and support vector machines (SVM), using two different population samples. Using photographs, the shape of the GSN in 229 samples from two assemblages (documented collections of a Euroamerican population from the Maxwell Museum, University of New Mexico, and a Hispanic population from Universidad Nacional Autónoma de México, Mexico City) was segmented automatically and evaluated using six curve representations. The optimal dimensionality for each representation was determined by finding the best sex classification. The classification accuracy of the six curve representations in our study was similar but the highest and concurrently homologous cross-validated accuracy of 92% was achieved for a pooled sample using Fourier coefficient and Legendre polynomial methods. The success rate of our classification was influenced by the number of semilandmarks or coefficients and was only slightly affected by GSN marginal point positions. The intrapopulation variability of the female GSN shape was significantly lower compared with the male variability, possibly as a consequence of the intense selection pressure associated with reproduction. Males were misclassified more often than females. Our results show that by using a suitable GSN curve representation, a GM approach, and SVM analysis, it is possible to obtain a robust separation between the sexes that is stable for a multipopulation sample.
- MeSH
- analýza hlavních komponent MeSH
- antropologie fyzická metody MeSH
- lidé MeSH
- pánevní kosti anatomie a histologie MeSH
- pohlavní dimorfismus MeSH
- reprodukovatelnost výsledků MeSH
- support vector machine MeSH
- určení pohlaví podle kostry metody MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Image co-registration requires special software, which is usually available for Unix workstations. This work presents two programs running under MS Windows, one for study co-registration and one for template creation. The co-registration can be done by minimising/maximising the count difference, squared difference, shape and mutual information. The quality of the fit can be estimated by evaluating the contours with different tools. The aligned images can be used for template creation. Both programs can be downloaded from http://www.homolka.cz/nm.
