Skip navigation
Please use this identifier to cite or link to this item:
Title: Distances and algorithms to compare sets of shapes for automated biological morphometrics
Authors: Puente, Jesus
Advisors: Daubechies, Ingrid C
Tian, Gang
Contributors: Applied and Computational Mathematics Department
Keywords: Morphometrics
Subjects: Applied mathematics
Computer science
Issue Date: 2013
Publisher: Princeton, NJ : Princeton University
Abstract: In this thesis we present the Generalized Dataset Procrustes Distance, the basis for an automated framework to compare datasets of rigid biological shapes. It is based on a pairwise shape comparison algorithm that generalizes Procrustes Analysis in three dimensions and is closely related to the Iterative Closest Point (ICP) algorithm. It is not restricted by the topology of the shapes and is completely automatic, with only one parameter, namely the number of points to consider in each shape. The framework is based on an optimization problem on the whole dataset, which is assumed to consist of multiple similar shapes. Its backbone is the computation of the Minimum Spanning Tree of a complete graph where each vertex represents a shape in the dataset and the weights represent new distances between the shapes. Each of these distances is (relatively) expensive to compute, but we can reduce the number of shape comparisons required to compute the MST by exploiting that the distances satisfy the triangle inequality. This new framework provides morphologists with a tool to compare 3D scans of bones in a way that requires no human interaction and thus both reduces the time necessary for sample preparation and is free of human bias. Furthermore, the output of the algorithm can be interpreted by the well established procedures of the Morphometrics community, which facilitates its adoption and use. We solve the Generalized Dataset Procrustes Problem for three datasets of biological importance: the calcanei, astragali and grooming claws of primates.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Applied and Computational Mathematics

Files in This Item:
File Description SizeFormat 
Puente_princeton_0181D_10739.pdf28.91 MBAdobe PDFView/Download

Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.