CRANIAL MEASUREMENTS

Abstract

Human body exhibits regular age, sex and race dependent proportions amongstits various segments relative to its height. Knowledge of the cranial morphometry is importantfrom clinical and forensic view point. The stature of a person being genetically predeterminedis an inherent characteristic, the estimation of which is considered to be important assessmentin identification of human remains. Norms of regression formulae for calculation of height arerequired for different populations. Objectives: To document norms for cranial dimensions andpresent linear regression formulae for stature prediction in adult male and female populationof Southern Punjab. Place and duration of study: The study was conducted at the MultanMedical and Dental College, Multan and took about fourteen months to complete. Materialand methods: The study was conducted on 672 adult individuals (430 males and 242 females)from in and around the city of Multan in Punjab. Measurements of the head including maximumcranial length (glabella-inion length), maximum cranial breadth (maximum bi-parietal diameter)and maximum auricular head height were taken. Results were expressed as mean ± SD.Height was measured in standing anatomical position. Correlation coefficient of Pearsonwas used to find the relationship between various cranial dimensions using which the linearregression formulae to predict the stature were derived. Results: The mean height of the studypopulation was found to be significantly different between genders. The males appeared tobe considerably taller than females. The mean cranial length, cranial breadth and auricularhead height the measurements were larger significantly in the males as compared to females.Pearson’s correlation coefficient between stature and cranial measurements was found to behighly positive for both sexes. Linear regression formulae to predict the stature from the cranialdimensions were derived. Conclusion: The study is conducted to document norms for cranialdimensions and it presented gender specific linear regression models for stature prediction inadult South Punjab population.