Sizing the shape: understanding morphometrics.
OBJECTIVE
One of the most fundamental limitations associated with the conventional cephalometrics is its inability to delineate size from shape as it depends mainly on linear and angular measurements. However, the biological structures warrant greater description in terms of shape and form for better comparison of variation in a particular population. To overcome these shortcomings, morphometrics are now being employed for describing the biological structures in terms of quantifying the shape and form. Also, statistical analysis is being applied to find the variability of this form in the population. The present paper assesses the use of the Procuste superimposition technique and the subsequent form analysis by the principal component analysis (PCA).


METHODS
The lateral cephalograms of 10 adult females were taken from existing records, traced & digitized & then superimposed by means of procuste superimposition. A comparison was made with the conventional superimposition methods based on arbitrary reference planes like cranial base, FHP, SN. The statistical analysis for assessment of shape variability of the structures seen on the lateral cephalogram was done by calculating the principal components for 3 out of these 10 samples.


RESULTS
The conventional superimposition methods do not provide realistic picture of variation in the biological structures as they themselves are prone to variability even in a particular population.


CONCLUSIONS
Concepts in Morphometrics can be applied for the purpose of orthodontic assessment of a particular patient with regards to his craniofacial morphology. With the help of morphometrics, norms for a population can be determined based on all the kinds of variations present naturally in that particular population & individuals can thus be compared more realistically regarding the morphological variations.
Senior Resident, Department of Orthodontics and Dentofacial Orthopaedics, Maulana Azad Institute of Dental Sciences , New Delhi, India .
Neha
PCA (principal component analysis); Procustes; Statistical shape analysis