A Landmark-Free Method for Three-Dimensional Shape Analysis

Autor: Dennis E. Slice, Jana Makedonska, Benjamin J Pomidor
Jazyk: angličtina
Rok vydání: 2016
Předmět:
0106 biological sciences
0301 basic medicine
Computer science
lcsh:Medicine
Monkeys
01 natural sciences
Baboons
Medicine and Health Sciences
lcsh:Science
Musculoskeletal System
Distance measurement
Mammals
Measurement
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Metric (mathematics)
Physical Sciences
Vertebrates
Engineering and Technology
Anatomy
Algorithms
Research Article
Primates
Computer and Information Sciences
Imaging Techniques
Research and Analysis Methods
010603 evolutionary biology
Set (abstract data type)
03 medical and health sciences
Data visualization
Old World monkeys
Superimposition
Animals
Prototypes
Skeleton
Morphometrics
Landmark
business.industry
Morphometry
lcsh:R
Skull
Organisms
Iterative closest point
Biology and Life Sciences
Pattern recognition
Models
Theoretical
Data set
030104 developmental biology
Transformation (function)
Technology Development
lcsh:Q
Artificial intelligence
business
Mathematics
Zdroj: PLoS ONE, Vol 11, Iss 3, p e0150368 (2016)
PLoS ONE
Popis: Background The tools and techniques used in morphometrics have always aimed to transform the physical shape of an object into a concise set of numerical data for mathematical analysis. The advent of landmark-based morphometrics opened new avenues of research, but these methods are not without drawbacks. The time investment required of trained individuals to accurately landmark a data set is significant, and the reliance on readily-identifiable physical features can hamper research efforts. This is especially true of those investigating smooth or featureless surfaces. Methods In this paper, we present a new method to perform this transformation for data obtained from high-resolution scanning technology. This method uses surface scans, instead of landmarks, to calculate a shape difference metric analogous to Procrustes distance and perform superimposition. This is accomplished by building upon and extending the Iterative Closest Point algorithm. We also explore some new ways this data can be used; for example, we can calculate an averaged surface directly and visualize point-wise shape information over this surface. Finally, we briefly demonstrate this method on a set of primate skulls and compare the results of the new methodology with traditional geometric morphometric analysis.
Databáze: OpenAIRE
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