@article{Shen2009,
abstract = {Quantifying morphological shape is a fundamental issue in evolutionary biology. Recent technological advances (e.g., confocal microscopy, laser scanning, computer tomography) have made the capture of detailed three-dimensional (3D) morphological structure easy and cost-effective. In this article, we develop a 3D analytic framework (SPHARM - spherical harmonics) for modeling the shapes of complex morphological structures from continuous surface maps that can be produced by these technologies. Because the traditional SPHARM methodology has limitations in several of its processing steps, we present new algorithms for two SPHARM processing steps: spherical parameterization and SPHARM registration. These new algorithms allow for the numerical characterization of a much larger class of 3D models. We demonstrate the effectiveness of the method by applying it to modeling the cerci of Enallagma damselflies. {\textcopyright} 2009 The Author(s).},
author = {Shen, Li and Farid, Hany and McPeek, Mark A.},
doi = {10.1111/j.1558-5646.2008.00557.x},
file = {:Users/noshita/Documents/Mendeley Desktop//Shen, Farid, McPeek_2009_Evolution.pdf:pdf},
issn = {00143820},
journal = {Evolution},
keywords = {Enallagma cerci,Fourier descriptors,Morphological evolution,Shape analysis,Spherical harmonics},
mendeley-groups = {ktch-doc},
number = {4},
pages = {1003--1016},
pmid = {19154365},
title = {{Modeling three-dimensional morphological structures using spherical harmonics}},
volume = {63},
year = {2009}
}
@article{Ritchie1999,
abstract = {A procedure that rapidly generates an approximate parametric representation of macromolecular surface shapes is described. The parametrization is expressed as an expansion of real spherical harmonic basis functions. The advantage of using a parametric representation is that a pair of surfaces can be matched by using a quasi-Newton algorithm to minimize a suitably chosen objective function. Spherical harmonics are a natural and convenient choice of basis function when the task is one of search in a rotational search space. In particular, rotations of a molecular surface can be simulated by rotating only the harmonic expansion coefficients. This rotational property is applied for the first time to the 3-dimensional molecular similarity problem in which a pair of similar macromolecular surfaces are to be maximally superposed. The method is demonstrated with the superposition of antibody heavy chain variable domains. Special attention is given to computational efficiency. The spherical harmonic expansion coefficients are determined using fast surface sampling and integration schemes based on the tessellation of a regular icosahedron. Low resolution surfaces can be generated and displayed in under 10 s and a pair of surfaces can be maximally superposed in under 3 s on a contemporary workstation. {\textcopyright} 1999 John Wiley & Sons, Inc.},
author = {Ritchie, David W. and Kemp, Graham J.L.},
doi = {10.1002/(SICI)1096-987X(199903)20:4<383::AID-JCC1>3.0.CO;2-M},
file = {:Users/noshita/Documents/Mendeley Desktop//Ritchie, Kemp_1999_Journal of Computational Chemistry.pdf:pdf},
issn = {01928651},
journal = {Journal of Computational Chemistry},
keywords = {Function minimization,Icosahedral integration,Icosahedral sampling,Molecular similarity,Real rotation matrices,Spherical harmonics,Surface shape},
mendeley-groups = {ktch-doc},
number = {4},
pages = {383--395},
title = {{Fast computation, rotation, and comparison of low resolution spherical harmonic molecular surfaces}},
volume = {20},
year = {1999}
}
@misc{Dryden-shapes,
address = {Vienna, Austria},
annote = {Contributed package, Version 1.2.6.},
author = {Dryden, Ian},
booktitle = {R Foundation for Statistical Computing},
institution = {R Foundation for Statistical Computing},
mendeley-groups = {ktch-doc},
title = {{Shapes package}},
url = {ftp://ftp.uni-bayreuth.de/pub/math/statlib/R/CRAN/doc/packages/shapes.pdf},
year = {2015}
}
@book{TheGUDHIProject2021,
author = {{The GUDHI Project}},
edition = {3.4.1},
mendeley-groups = {ktch-doc},
publisher = {GUDHI Editorial Board},
title = {{GUDHI User and Reference Manual}},
url = {https://gudhi.inria.fr/doc/3.4.1/},
year = {2021}
}
@article{Iwata2002,
author = {Iwata, H. and Ukai, Y.},
doi = {10.1093/jhered/93.5.384},
file = {:Users/noshita/Documents/Mendeley Desktop/Iwata, Ukai_2002_The Journal of heredity.pdf:pdf},
issn = {00221503},
journal = {The Journal of heredity},
mendeley-groups = {ktch-doc},
number = {5},
pages = {384--385},
pmid = {12547931},
title = {{SHAPE: a computer program package for quantitative evaluation of biological shapes based on elliptic Fourier descriptors}},
volume = {93},
year = {2002}
}
@article{Tauzin2021,
abstract = {We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn{compatible API and state-of-the-art C++ implementations. The library's ability to handle various types of data is rooted in a wide range of preprocessing techniques, and its strong focus on data exploration and interpretability is aided by an intuitive plotting API. Source code, binaries, examples, and documentation can be found at http://github.com/giotto-ai/giotto-tda. &copy; 2021 Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Perez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, and Kathryn Hess.}},
archivePrefix = {arXiv},
arxivId = {2004.02551},
author = {Tauzin, Guillaume and Lupo, Umberto and Tunstall, Lewis and Perez, Julian Burella and Caorsi, Matteo and Medina-Mardones, Anibal M. and Dassatti, Alberto and Hess, Kathryn},
eprint = {2004.02551},
issn = {15337928},
journal = {Journal of Machine Learning Research},
keywords = {Data Exploration,Machine Learning,Mapper,Persistent Homology,Python,Topological Data Analysis},
mendeley-groups = {ktch-doc},
number = {ML},
pages = {1--7},
title = {{giotto-tda: A topological data analysis toolkit for machine learning and data exploration}},
volume = {22},
year = {2021}
}
@incollection{Vicory2018,
abstract = {SlicerSALT is an open-source platform for disseminating state-of-the-art methods for performing statistical shape analysis. These methods are developed as 3D Slicer extensions to take advantage of its powerful underlying libraries. SlicerSALT itself is a heavily customized 3D Slicer package that is designed to be easy to use for shape analysis researchers. The packaged methods include powerful techniques for creating and visualizing shape representations as well as performing various types of analysis.},
author = {Vicory, Jared and Pascal, Laura and Hernandez, Pablo and Fishbaugh, James and Prieto, Juan and Mostapha, Mahmoud and Huang, Chao and Shah, Hina and Hong, Junpyo and Liu, Zhiyuan and Michoud, Loic and Fillion-Robin, Jean Christophe and Gerig, Guido and Zhu, Hongtu and Pizer, Stephen M. and Styner, Martin and Paniagua, Beatriz},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-04747-4_6},
file = {:Users/noshita/Documents/Mendeley Desktop//Vicory et al._2018_Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in.pdf:pdf},
isbn = {9783030047467},
issn = {16113349},
keywords = {Shape analysis,Software,Statistics},
mendeley-groups = {ktch-doc},
pages = {65--72},
publisher = {Springer International Publishing},
title = {{SlicerSALT: Shape AnaLysis Toolbox}},
url = {http://dx.doi.org/10.1007/978-3-030-04747-4_6 http://link.springer.com/10.1007/978-3-030-04747-4_6},
volume = {11167 LNCS},
year = {2018}
}
@article{Styner2006,
abstract = {Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology. The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling   T^2   T 2     two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives.New NITRC page for binary distribution},
author = {Styner, Martin and Oguz, Ipek and Xu, Shun and Brechb{\"{u}}hler, Christian and Pantazis, Dimitrios and Levitt, James J and Shenton, Martha E and Gerig, Guido},
doi = {10.54294/owxzil},
file = {:Users/noshita/Documents/Mendeley Desktop//Styner et al._2006_The insight journal.pdf:pdf},
issn = {2327-770X},
journal = {The insight journal},
mendeley-groups = {ktch-doc},
number = {1071},
pages = {242--250},
pmid = {21941375},
title = {{Framework for the Statistical Shape Analysis of Brain Structures using SPHARM-PDM.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21941375%0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3062073},
year = {2006}
}
@article{KUHL:1982bq,
abstract = {A direct procedure for obtaining the Fourier coefficients of a chain-encoded contour is presented. Advantages of the procedure are that it does not require integration or the use of fast Fourier transform techniques, and that bounds on the accuracy of the image contour reconstruction are easy to specify. Elliptic properties of the Fourier coefficients are shown and used for a convenient and intuitively pleasing procedure of normalizing a Fourier contour representation. Extension of the contour representation to arbitrary objects at arbitrary aspect angle is discussed. The procedures have direct application to a variety of pattern recognition problems that involve analysis of well-defined image contours. {\textcopyright} 1982.},
author = {Kuhl, Frank P. and Giardina, Charles R.},
doi = {10.1016/0146-664X(82)90034-X},
issn = {0146664X},
journal = {Computer Graphics and Image Processing},
mendeley-groups = {ktch-doc},
number = {3},
pages = {236--258},
title = {{Elliptic Fourier features of a closed contour}},
volume = {18},
year = {1982}
}
@book{Claude2008,
abstract = {Instructional Book for using R for programming},
address = {New York, NY},
author = {Claude, Julien},
booktitle = {Morphometrics with R},
doi = {10.1007/978-0-387-77789-4},
isbn = {978-0-387-77789-4},
mendeley-groups = {ktch-doc},
publisher = {Springer New York},
title = {{Morphometrics with R}},
url = {http://link.springer.com/10.1007/978-0-387-77789-4},
year = {2008}
}
@article{Klingenberg2011,
abstract = {Increasingly, data on shape are analysed in combination with molecular genetic or ecological information, so that tools for geometric morphometric analysis are required. Morphometric studies most often use the arrangements of morphological landmarks as the data source and extract shape information from them by Procrustes superimposition. The MorphoJ software combines this approach with a wide range of methods for shape analysis in different biological contexts. The program offers an integrated and user-friendly environment for standard multivariate analyses such as principal components, discriminant analysis and multivariate regression as well as specialized applications including phylogenetics, quantitative genetics and analyses of modularity in shape data. MorphoJ is written in Java and versions for the Windows, Macintosh and Unix/Linux platforms are freely available from {\textcopyright} 2010 Blackwell Publishing Ltd.},
author = {Klingenberg, Christian Peter},
doi = {10.1111/j.1755-0998.2010.02924.x},
file = {:Users/noshita/Documents/Mendeley Desktop//Klingenberg_2011_Molecular Ecology Resources.pdf:pdf},
isbn = {1755-0998 (Electronic)\r1755-098X (Linking)},
issn = {1755098X},
journal = {Molecular Ecology Resources},
keywords = {Geometric morphometrics,Multivariate statistics,Phylogeny,Procrustes superimposition,Quantitative genetics,Shape},
mendeley-groups = {ktch-doc},
number = {2},
pages = {353--357},
pmid = {21429143},
title = {{MorphoJ: An integrated software package for geometric morphometrics}},
url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=21429143},
volume = {11},
year = {2011}
}
@article{Bonhomme2013,
abstract = {We introduce here Momocs, a package intended to ease and popularize modern mor-phometrics with R, and particularly outline analysis, which aims to extract quantitative variables from shapes. It mostly hinges on the functions published in the book entitled Modern Morphometrics Using R by Claude (2008). From outline extraction from raw data to multivariate analysis, Momocs provides an integrated and convenient toolkit to students and researchers who are, or may become, interested in describing the shape and its variation. The methods implemented so far in Momocs are introduced through a simplistic case study that aims to test if two sets of bottles have different shapes.},
author = {Bonhomme, Vincent and Picq, Sandrine and Gaucherel, C{\'{e}}dric and Claude, Julien},
doi = {10.18637/jss.v056.i13},
file = {:Users/noshita/Documents/Mendeley Desktop/Bonhomme et al._2013_Journal of Statistical Software.pdf:pdf},
issn = {15487660},
journal = {Journal of Statistical Software},
keywords = {Comparison of shapes,Fourier transform,Modern morphometrics,Outline analysis,R,Thin plate splines},
mendeley-groups = {ktch-doc},
number = {13},
pages = {1--24},
title = {{Momocs: Outline analysis using R}},
url = {http://www.jstatsoft.org/v56/i13/paper%5Cnpapers3://publication/uuid/9FA27917-592B-4216-85D5-BA4A85B8E698},
volume = {56},
year = {2014}
}
@article{Adams2013,
abstract = {Many ecological and evolutionary studies seek to explain patterns of shape variation and its covariation with other variables. Geometric morphometrics is often used for this purpose, where a set of shape variables are obtained from landmark coordinates following a Procrustes superimposition. We introduce geomorph: a software package for performing geometric morphometric shape analysis in the r statistical computing environment. Geomorph provides routines for all stages of landmark-based geometric morphometric analyses in two and three-dimensions. It is an open source package to read, manipulate, and digitize landmark data, generate shape variables via Procrustes analysis for points, curves and surfaces, perform statistical analyses of shape variation and covariation, and to provide graphical depictions of shapes and patterns of shape variation. An important contribution of geomorph is the ability to perform Procrustes superimposition on landmark points, as well as semilandmarks from curves and surfaces. A wide range of statistical methods germane to testing ecological and evolutionary hypotheses of shape variation are provided. These include standard multivariate methods such as principal components analysis, and approaches for multivariate regression and group comparison. Methods for more specialized analyses, such as for assessing shape allometry, comparing shape trajectories, examining morphological integration, and for assessing phylogenetic signal, are also included. Several functions are provided to graphically visualize results, including routines for examining variation in shape space, visualizing allometric trajectories, comparing specific shapes to one another and for plotting phylogenetic changes in morphospace. Finally, geomorph participates to make available advanced geometric morphometric analyses through the r statistical computing platform. {\textcopyright} 2013 The Authors. Methods in Ecology and Evolution {\textcopyright} 2013 British Ecological Society.},
author = {Adams, Dean C. and Ot{\'{a}}rola-Castillo, Erik},
doi = {10.1111/2041-210X.12035},
file = {:Users/noshita/Documents/Mendeley Desktop/Adams, Ot{\'{a}}rola-Castillo_2013_Methods in Ecology and Evolution.pdf:pdf},
issn = {2041210X},
journal = {Methods in Ecology and Evolution},
keywords = {Evolutionary biology,Macroevolution,Statistics},
mendeley-groups = {ktch-doc},
number = {4},
pages = {393--399},
title = {{Geomorph: An r package for the collection and analysis of geometric morphometric shape data}},
url = {http://doi.wiley.com/10.1111/2041-210X.12035},
volume = {4},
year = {2013}
}
@book{bookstein1997morphometric,
abstract = {Morphometrics is the statistical study of biological shape and shape change. Its richest data are landmarks, points such as the bridge of the nose that have biological names as well as geometric locations. This book is the first systematic survey of morphometric methods for landmark data. The methods presented here combine conventional multivariate statistical analysis with themes from plane and solid geometry and from biomathematics to support biological insights into the features of many different organs and organisms. This book will be of value to applied statisticians and geometers, as well as to all biological and biomedical researchers who need quantitative analyses of information from biomedical images.},
author = {Bookstein, Fred L.},
booktitle = {Morphometric Tools for Landmark Data},
doi = {10.1017/cbo9780511573064},
isbn = {9780521383851},
issn = {01621459},
mendeley-groups = {ktch-doc},
month = {jan},
number = {445},
pages = {340},
publisher = {Cambridge University Press},
series = {Geometry and Biology},
title = {{Morphometric Tools for Landmark Data}},
url = {https://books.google.co.jp/books?id=amwT1ddIDwAC https://www.cambridge.org/core/product/identifier/9780511573064/type/book},
volume = {94},
year = {1992}
}
