Morphometric Analysis – Geography – UGC NET – Notes

Introduction

• The word ‘Morphometry’ means the measurement of the external form and ‘Analysis’ means detail evaluation. Morphometric Analysis in Geomorphology means detail evaluation of landforms through mathematical measurement. Mathematical or quantitative measurement helps us in analyzing the landforms accurately for any planning and development purposes. Morphometric Analysis is also very useful as it quantifies the landform features of evolutionary significance.
• Morphometric analysis is a type of cognitive description. It involves a space-time language rather than a property language. Morphometric analysis thus provides a framework within which the geographer examines shapes and forms in space.
• Morphometric analysis can lead to certain types of predictive and simulation models. In this analysis, stress is on measurement whereas studies of landscape morphology usually take the form of cognitive description. The locational theories and the central places are the results of this type of analysis. Geometrical predictions of this nature have had increasing significance in geography.
• Morphometrics refers to the quantitative analysis of form, which is a concept that encompasses both the size and shape of an organism or organ. In neuroimaging, morphometric approaches are typically used to characterize differences among populations of subjects or to identify features that correlate with some measurement of interest. These measurements may be clinical scores, test score results, genetic measurements, or anything else of interest to the investigator.
• The usual approaches involve extracting anatomical features or descriptors from MRI data of the subjects and performing some form of statistical analysis on them. This article concerns tensor-based morphometric techniques, which involve analyzing features that principally relate to the relative volumes of structures, as estimated by image registration. The mathematics involved in morphometrics can be quite complicated.
• Morphometrics has a long history throughout many areas of biology. Most applications do not have the benefit of imaging devices that enable 3-D volumetric scans to be collected, so generally focus on working with things that can easily be measured from the organ or organism itself.
• Traditional approaches were limited to measures such as lengths, widths, angles, and distances, which were subjected to statistical analysis. When technological advances made it easier to record the locations of landmarks, a number of new morphometric approaches appeared, which were largely inspired by the work of Thompson.
• Instead of analyzing lengths, widths, etc., the new geometric morphometrics involved analyzing landmark coordinates in space, after first correcting for pose (and possibly size). Instead of treating data in a feature by feature way, multivariate analyses of landmark positions, or of thin-plate spline coefficients, preserved geometric relationships among all the points.
• Most areas of biology are limited to making measurements on the outside surface of whatever organ or organism they chose to study, whereas neuroimagers have the advantage of being able to measure a much wider variety of things inside the brain.
• Some brain morphometric studies involve volumes obtained by manually tracing regions in scans, although these are mostly limited to a handful of structures with clear boundaries. While there may be a wealth of findings pertaining to these particular structures (e.g., ventricles and hippocampi), other brain regions can easily be neglected. It would be a mistake to assume that neurological disorders only affect those structures we can manually outline.
• Manual tracing of a structure also ignores the potential variability within that structure, for example, one hippocampal subfield could be relatively smaller and another relatively larger with no detectable change in the overall volume.
• Neuroimagers tend not to use much landmark data. In part, this is because there are few discrete and readily identifiable points within the brain. Instead, the field relies on correspondences estimated by automatic or semiautomatic image registration approaches. Providing the same software and settings are used, such approaches should lead to fully reproducible results, irrespective of who runs it.
• The following section briefly describes some of the statistical testing procedures that may be applied to morphometric features. This is followed by a section about the types of features that are typically extracted for tensor-based morphometric (TBM) studies.

Forms of Morphomerics

Three general approaches to form are usually distinguished:

1. Traditional Morphometrics,
2. Landmark-based Morphometrics and
3. Outline-based Morphometrics

“Traditional” Morphometrics

• Traditional morphometrics analyzes lengths, widths, masses, angles, ratios and areas. In general, traditional morphometric data are measurements of size. A drawback of using many measurements of size is that most will be highly correlated; as a result, there are few independent variables despite the many measurements. For instance, tibia length will vary with femur length and also with humerus and ulna length and even with measurements of the head.
• Traditional morphometric data are nonetheless useful when either absolute or relative sizes are of particular interest, such as in studies of growth. These data are also useful when size measurements are of theoretical importance such as body mass and limb crosssectional area and length in studies of functional morphology.
• However, these measurements have one important limitation: they contain little information about the spatial distribution of shape changes across the organism. They are also useful when determining the extent to which certain pollutants have affected an individual. these indices include the hepatosomatic index, gonadosomatic index and also the condition factors.

Landmark-based Geometric Morphometrics

• In landmark-based geometric morphometrics, the spatial information missing from traditional morphometrics is contained in the data, because the data are coordinates of landmarks: discrete anatomical loci that are arguably homologous in all individuals in the analysis (i.e. they can be regarded as the “same” point in each specimens in the study).
• For example, where two specific sutures intersect is a landmark, as are intersections between veins on an insect wing or leaf, or foramina, small holes through which veins and blood vessels pass. Landmark-based studies have traditionally analyzed 2D data, but with the increasing availability of 3D imaging techniques, 3D analyses are becoming more feasible even for small structures such as teeth.
• Finding enough landmarks to provide a comprehensive description of shape can be difficult when working with fossils or easily damaged specimens. That is because all landmarks must be present in all specimens, although coordinates of missing landmarks can be estimated. The data for each individual consists of a configuration of landmarks.
• There are three recognized categories of landmarks. Type 1 landmarks are defined locally, i.e. in terms of structures close to that point; for example, an intersection between three sutures, or intersections between veins on an insect wing are locally defined and surrounded by tissue on all sides.
• Type 3 landmarks, in contrast, are defined in terms of points far away from the landmark, and are often defined in terms of a point “furthest away” from another point. Type 2 landmarks are intermediate; this category includes points such as the tip structure, or local minima and maxima of curvature.
• They are defined in terms of local features, but they are not surrounded on all sides. In addition to landmarks, there are semilandmarks, points whose position along a curve is arbitrary but which provide information about curvature in two or three dimensions.