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Applications of Geometric Morphometrics and Computational Imaging

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A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 27410

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Dr. Miguel Angel Maté-González

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Departamento de Ingeniería Cartográfica y del Terreno, Universidad de Salamanca, 37008 Salamanca, Spain
Interests: cartographic; geodetic and photogrammetric engineering
Special Issues, Collections and Topics in MDPI journals

Dr. Julia Aramendi

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Guest Editor

McDonald Institute for Archaeological Research, Department of Archaeology, University of Cambridge, Cambridge CB2 3DZ, UK
Interests: biological anthropology; archaeology; palaeoanthropology; human evolution; geometric morphometrics; biomechanics; virtual anatomy; Artificial Intelligence; tafonomy

Special Issue Information

Dear Colleagues,

In recent years, an increasing use of computational imaging techniques and of multivariate statistics, including those involved in geometric morphometrics (GM), has been observed in a wide range of disciplines.

On one hand, the digitization of organisms and objects using different techniques has allowed the collection of information at different levels. This has been particularly important in order to perform studies from different perspectives, including the macro and micro scale, as well as in contexts where internal structural data are also required or where the elements under study are particularly fragile.

On the other hand, the development of GM and its application to studies beyond biology has changed analyses of size and shape over the past decades, by substituting the traditional descriptive methods based on linear measurements for landmark-based methods by which it is possible to study size and shape variation and covariation, visualizing the results in terms of warpings and transformation grids.

In this context, original research including the application of imaging techniques in combination with advanced statistical methods such as geometric morphometrics are welcome in the present Special Issue. Scientific contributions can be related to works in biological or paleontological sciences and to procedures for the statistical analysis of morphological/geometrical features.

Dr. Miguel Ángel Maté-González
Dr. Julia Aramendi
Guest Editors

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Keywords

geometric morphometrics
computational imaging
statistics
computer vision
machine learning and deep learning
biological anthropology
biological science
medicine
paleontology
zooarchaeology
taphonomy

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Published Papers (12 papers)

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Research

14 pages, 8164 KiB

Open AccessArticle

Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation

by Sungkwan Youm and Sunghyun Go

Appl. Sci. 2023, 13(20), 11254; https://doi.org/10.3390/app132011254 - 13 Oct 2023

Viewed by 980

Abstract

Images captured by drones are increasingly used in various fields, including geographic information management. This study evaluates a procedure that incorporates active learning semantic segmentation for verifying the building registration ledger. Several semantic segmentation techniques were evaluated to extract building information, with ResNet identified as the most effective method for accurately recognizing building roofs. Using active learning, the training data were refined by removing instances with low similarity, leading to improved network performance of the model. The procedure was demonstrated to identify discrepancies between the building information system and the inferred label images, as well as to detect labeling errors on a training dataset. Through this research, the geographic information system dataset is enhanced with minimal human oversight, offering significant potential for urban planning and building detection advancements. Full article

(This article belongs to the Special Issue Applications of Geometric Morphometrics and Computational Imaging)

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10 pages, 2416 KiB

Open AccessArticle

The Arrangement of the Osteons and Kepler’s Conjecture

by Marco Zedda

Appl. Sci. 2023, 13(8), 5170; https://doi.org/10.3390/app13085170 - 21 Apr 2023

Cited by 4 | Viewed by 1429

Abstract

The studies of bone tissue have mainly highlighted the morphometrical characteristics of the osteons, rather than their spatial distribution. This work aimed to verify if the topographical distribution of the osteons responds to geometrical order. From an analysis of hundreds of bone sections of domestic and wild mammals collected over 60 years, it is evident that the spatial distribution of osteons varies from a random arrangement in the irregular Haversian tissue to an ordered geometric arrangement in the dense Haversian tissue. In this work, a new method of classification of Haversian bone tissue was introduced based on the number of points of contact that the perimeter of an osteon has with neighboring osteons. When the functional commitment of the bone is maximum to resist biomechanical stresses, the osteons are smaller and crammed adjacent to each other as if to occupy less space. Their spatial arrangement, in this case, reminds us of Kepler’s conjecture, which predicts the ideal arrangement that spheres must have to occupy as little space as possible. The conjecture was elaborated by Kepler in the Seventeenth Century to solve the practical problem linked to the need to transport the largest number of cannonballs in warships. Full article

(This article belongs to the Special Issue Applications of Geometric Morphometrics and Computational Imaging)

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9 pages, 894 KiB

Open AccessArticle

Morphological Integration on the Calcaneum of Domestic Sheep (Ovis aries Linnaeus, 1758)—A Geometric Morphometric Study

by Pere Miquel Parés-Casanova, Carme Rissech, Simon Davis and Lluís Lloveras

Appl. Sci. 2023, 13(7), 4177; https://doi.org/10.3390/app13074177 - 25 Mar 2023

Cited by 1 | Viewed by 1508

Abstract

Morphological integration and modularity refer to the degree of covariation between the different components of an anatomical structure. Modularity refers to structures that have components which covary strongly, but at the same time are relatively independent. Integration refers to the coordinated variation of the components of a functional anatomical structure. The hindlimb basipodium is a complex functional structure. The integration of its parts must arise from a coordinated development and functionality. Our objective in this study was to study the levels of integration of two modules on the calcaneum in domestic sheep. The calcaneum develops from two different centres. One gives rise to the body and its process while the other gives rise to the distal half of the bone. The hypothesis of modularity of two parts of the calcaneum was tested using the Escoufier RV coefficient and an analysis of two blocks of Partial Least Squares. These allowed us to evaluate the level of morphological integration. For this purpose, digital images of the medial aspect of complete calcanea of 47 domestic sheep (Ovis aries) were used. Twenty 2D coordinates of homologous anatomical landmarks (4 for the body and the process and 16 for the distal half of the bone) were collected as morphometric data. These were studied using geometric morphometrics. The results indicated good evidence of modular organization and a medium morphological integration between the two modules. Thus, according to our results, the two modules seem to exist and behave in a rather independent way. They appear to share functions rather than precursors of development. This study is important in order to elucidate the underlying factors in the processes of the development of the sheep calcaneum. Full article

(This article belongs to the Special Issue Applications of Geometric Morphometrics and Computational Imaging)

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14 pages, 2907 KiB

Open AccessArticle

Geometric Morphometrics and Machine Learning Models Applied to the Study of Late Iron Age Cut Marks from Central Spain

by Miguel Ángel Maté-González, Verónica Estaca-Gómez, Julia Aramendi, Cristina Sáez Blázquez, Jesús Rodríguez-Hernández, José Yravedra Sainz de los Terreros, Gonzalo Ruiz-Zapatero and Jesús R. Álvarez-Sanchís

Appl. Sci. 2023, 13(6), 3967; https://doi.org/10.3390/app13063967 - 21 Mar 2023

Cited by 4 | Viewed by 2255

Abstract

Recently the incorporation of artificial intelligence has allowed the development of valuable methodological advances in taphonomy. Some studies have achieved great precision in identifying the carnivore that produced tooth marks. Additionally, other works focused on human activity have managed to specify what type of tool or raw material was used in the filleting processes identified at the sites. Through the use of geometric morphometrics and machine learning techniques, the present study intends to analyze the cut marks of the Ulaca oppidum (Solosancho, Ávila, Spain) in order to identify the type of tools used during carcass modification. Although the Ulaca oppidum is an Iron Age site, the results suggest that most of the cut marks were produced with flint tools. Full article

(This article belongs to the Special Issue Applications of Geometric Morphometrics and Computational Imaging)

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23 pages, 5782 KiB

Open AccessArticle

An Evaluation of Landmark-Based Methods to Explore Tooth Score Morphology: A Case Study on Felids and Hyenids

by Mari Carmen Arriaza, Julia Aramendi, Lloyd A. Courtenay, Miguel Ángel Maté-González, Darío Herranz-Rodrigo, Diego González-Aguilera and José Yravedra

Appl. Sci. 2023, 13(6), 3864; https://doi.org/10.3390/app13063864 - 17 Mar 2023

Cited by 3 | Viewed by 1420

Abstract

Taphonomic studies aim to identify the modifying agents that intervene in bone assemblages found at archaeopaleontological sites. Carnivores may modify, accumulate, or scavenge skeletal parts inflicting tooth marks, including scores, on the cortical surface. Several works have studied tooth score morphology to discern which carnivore group modified the bone assemblages, achieving different results. In the present study, different methods based on the use of landmarks and semilandmarks have been tested to describe and analyze the score profile cross-sections of spotted and brown hyenas, leopards, and lions. According to our results, the already published seven-landmark method is useful in order to differentiate between carnivore species from different families (e.g., felids and hyenids). Meanwhile, felid species (e.g., leopards and lions) cannot be consistently distinguished using any of the methods tested here. In contrast, hyenid species can be morphologically differentiated. On the other hand, the use of semilandmarks does not generally improve morphological characterization and distinction, but low numbers of landmarks and the inclusion of the score’s deepest point might provide the best results when semi-automatic semilandmark models are preferred to avoid sampling biases. Full article

(This article belongs to the Special Issue Applications of Geometric Morphometrics and Computational Imaging)

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10 pages, 2745 KiB

Open AccessArticle

Determination of Morphogeometric Patterns in Individuals with Total Mandibular Edentulism in the Interforaminal Region from Cone Beam Computed Tomography (CBCT) Scans: A Pilot Study

by Liliann Abarza, Pablo Acuña-Mardones, Cristina Sanzana-Luengo and Víctor Beltrán

Appl. Sci. 2022, 12(8), 3813; https://doi.org/10.3390/app12083813 - 10 Apr 2022

Cited by 1 | Viewed by 2136

Abstract

The aim of this study was to determine the morphogeometric patterns of the interforaminal region from cone beam computed tomography (CBCT) scans of individuals with total mandibular edentulism. CBCT images were obtained from 40 patients with total edentulism who are older (12 men and 28 women; average age of 69.5 ± 9.4 years) and who wore a non-implant-supported, lower, removable, total prosthesis. We conducted a two-dimensional (2D) morphogeometric analysis of the Digital Imaging and Communication in Medicine (DICOM) files from the CBCT scans, and five equidistant cross sections were planned. For the three-dimensional (3D) morphogeometric analysis, standard triangular language (STL) files were obtained after segmentation of the interforaminal mandibular region, and four anatomical landmarks and their respective curves were digitized. The patterns among the shapes were determined using principal component analysis (PCA) on MorphoJ software (version 1.07a). The results of the 2D morphogeometric analyses for PCA of the interforaminal mandibular paramedian region were PC1 or elongated drop shape, 54.78%; PC2 or wineskin shape, 17.65%; PC3 or pear shape, 11.77%; and PC4 or eggplant shape, 5.71%, and those for PCA of the symphyseal region were PC1 or elongated drop shape, 62.13%; PC2 or ovoid shape, 11.64%; PC3 or triangular shape, 9.71%; and PC4 or tuber shape, 4.96%. The results of the 3D morphogeometric analyses for the interforaminal hemimandibular region were PC1, 59.83%; PC2, 10.39%; PC3, 7.67%; and PC4, 5.09%. This study provides relevant information for future clinical guidelines on prosthetics and implants, in addition to proposing the use of new technologies that support diagnosis and treatment in patients with edentulism. Full article

(This article belongs to the Special Issue Applications of Geometric Morphometrics and Computational Imaging)

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26 pages, 6728 KiB

Open AccessArticle

Investigating Shape Variation Using Generalized Procrustes Analysis and Machine Learning

by Wilfried Wöber, Lars Mehnen, Manuel Curto, Papius Dias Tibihika, Genanaw Tesfaye and Harald Meimberg

Appl. Sci. 2022, 12(6), 3158; https://doi.org/10.3390/app12063158 - 20 Mar 2022

Cited by 3 | Viewed by 3054

Abstract

The biological investigation of a population’s shape diversity using digital images is typically reliant on geometrical morphometrics, which is an approach based on user-defined landmarks. In contrast to this traditional approach, the progress in deep learning has led to numerous applications ranging from specimen identification to object detection. Typically, these models tend to become black boxes, which limits the usage of recent deep learning models for biological applications. However, the progress in explainable artificial intelligence tries to overcome this limitation. This study compares the explanatory power of unsupervised machine learning models to traditional landmark-based approaches for population structure investigation. We apply convolutional autoencoders as well as Gaussian process latent variable models to two Nile tilapia datasets to investigate the latent structure using consensus clustering. The explanatory factors of the machine learning models were extracted and compared to generalized Procrustes analysis. Hypotheses based on the Bayes factor are formulated to test the unambiguity of population diversity unveiled by the machine learning models. The findings show that it is possible to obtain biologically meaningful results relying on unsupervised machine learning. Furthermore we show that the machine learning models unveil latent structures close to the true population clusters. We found that

80 %

of the true population clusters relying on the convolutional autoencoder are significantly different to the remaining clusters. Similarly,

60 %

of the true population clusters relying on the Gaussian process latent variable model are significantly different. We conclude that the machine learning models outperform generalized Procrustes analysis, where

16 %

of the population cluster was found to be significantly different. However, the applied machine learning models still have limited biological explainability. We recommend further in-depth investigations to unveil the explanatory factors in the used model. Full article

(This article belongs to the Special Issue Applications of Geometric Morphometrics and Computational Imaging)

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12 pages, 11344 KiB

Open AccessArticle

Parallel Evolution of Allometric Trajectories of Trophic Morphology between Sympatric Morphs of Mesoamerican Astyanax (Characidae)

by Carlos A. Garita-Alvarado and Claudia Patricia Ornelas-García

Appl. Sci. 2021, 11(17), 8020; https://doi.org/10.3390/app11178020 - 30 Aug 2021

Cited by 4 | Viewed by 1678

Abstract

Parallel evolution of the body shape and trophic-related traits has been detected between sympatric pairs of lake-dwelling characin fishes in Mesoamerica. Here, we evaluated the variation in and the ontogenetic allometric trajectories of trophic morphology between sympatric Astyanax morphs (elongate and deep-body) in two geographic systems, Lake Catemaco (Mexico) and San Juan River basin (Nicaragua and Costa Rica). Using geometric morphometrics, we determined the shape variation and disparity in the premaxillary bone, and the patterns of allometric trajectories between morphs in each system. We found a higher differentiation and disparity in the premaxilla shape between morphs from San Juan River basin than between the Lake Catemaco ones. We found shared (parallel evolution) patterns of divergence between systems, which included allometric trajectories showing a positive correlation between the premaxilla shape and log centroid size, as well as trajectories being extended in the elongated-body morph (truncated in the deep-body morph). Regarding the unique patterns of divergence, we recovered parallel allometric trajectories between morphs from Lake Catemaco, while the San Juan River basin morphs showed divergent trajectories. Our results are congruent with the hypothesis that divergence in trophic morphology can be considered a triggering factor in the divergence in the genus Astyanax from Mesoamerica. Full article

(This article belongs to the Special Issue Applications of Geometric Morphometrics and Computational Imaging)

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13 pages, 19643 KiB

Open AccessArticle

Fluctuating Asymmetry and Stress in Macaca fuscata: Does Captivity Affect Morphology?

by Federica Landi, Fabio Alfieri, Ian Towle, Antonio Profico and Alessio Veneziano

Appl. Sci. 2021, 11(17), 7879; https://doi.org/10.3390/app11177879 - 26 Aug 2021

Cited by 4 | Viewed by 2510

Abstract

Fluctuating Asymmetry (FA) in morphology is used as a proxy for developmental instability in response to stress factors. FA has important implications for understanding the impact of differential environments and stressors on the skeletal phenotype. Here, we explore FA in the mandibular morphology of wild and captive Macaca fuscata to detect differences induced by the captive environment. We use two different approaches in Geometric Morphometrics to characterise the degree and patterns of FA and Directional Asymmetry (DA) based on 3D mandibular landmarks. Our results show that the wild and captive groups exhibit morphological dissimilarities in the symmetric component of shape while no significant degree of asymmetry (fluctuating or directional) was detected. Based on our results and on previous literature on the subject, we suggest that (I) captivity is likely to affect the mandibular morphology of M. fuscata; (II) FA may not be a suitable indicator to detect stress in the conditions analysed; and that (III) the mandible may not be the ideal region to study asymmetry because of its functional nature. Full article

(This article belongs to the Special Issue Applications of Geometric Morphometrics and Computational Imaging)

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10 pages, 1945 KiB

Open AccessFeature PaperArticle

New Geometric Morphometric Insights in Digital Taphonomy: Analyses into the Sexual Dimorphism of Felids through Their Tooth Pits

by Darío Herranz-Rodrigo, Silvia J. Tardáguila-Giacomozzi, Lloyd A. Courtenay, Juan-José Rodríguez-Alba, Antonio Garrucho, Jesús Recuero and José Yravedra

Appl. Sci. 2021, 11(17), 7848; https://doi.org/10.3390/app11177848 - 26 Aug 2021

Cited by 5 | Viewed by 1977

Abstract

Recent studies using geometric morphometrics for taphonomy have yielded interesting results, opening new horizons of research in both archaeological and paleontological sites. Here we present the analysis of tooth pits left by male and female individuals of two different carnivore species (Panthera tigris and Panthera pardus) in order to see if sexual dimorphism influences the morphology of tooth pit marks. In the process, 3D-scanning and applied statistics were used. Based on samples derived from two individuals of different sexes, the present results indicate sexual dimorphism in these felid species to not be a conditioning factor of tooth pit morphology. Full article

(This article belongs to the Special Issue Applications of Geometric Morphometrics and Computational Imaging)

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13 pages, 2620 KiB

Open AccessArticle

Use of Generative Adversarial Networks (GAN) for Taphonomic Image Augmentation and Model Protocol for the Deep Learning Analysis of Bone Surface Modifications

by Manuel Domínguez-Rodrigo, Ander Fernández-Jaúregui, Gabriel Cifuentes-Alcobendas and Enrique Baquedano

Appl. Sci. 2021, 11(11), 5237; https://doi.org/10.3390/app11115237 - 4 Jun 2021

Cited by 6 | Viewed by 2918

Abstract

Deep learning models are based on a combination of neural network architectures, optimization parameters and activation functions. All of them provide exponential combinations whose computational fitness is difficult to pinpoint. The intricate resemblance of the microscopic features that are found in bone surface modifications make their differentiation challenging, and determining a baseline combination of optimizers and activation functions for modeling seems necessary for computational economy. Here, we experiment with combinations of the most resolutive activation functions (relu, swish, and mish) and the most efficient optimizers (stochastic gradient descent (SGD) and Adam) for bone surface modification analysis. We show that despite a wide variability of outcomes, a baseline of relu–SGD is advised for raw bone surface modification data. For imbalanced samples, augmented datasets generated through generative adversarial networks are implemented, resulting in balanced accuracy and an inherent bias regarding mark replication. In summary, although baseline procedures are advised, these do not prevent to overcome Wolpert’s “no free lunch” theorem and extend it beyond model architectures. Full article

(This article belongs to the Special Issue Applications of Geometric Morphometrics and Computational Imaging)

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11 pages, 2304 KiB

Open AccessArticle

Geometric and Volumetric Measurements of Orbital Structures in CT Scans in Thyroid Eye Disease Classification

by Yuekun Bao, Zhihui Zhang, Cheng Li, Huan Ma, Pan Yin, Yinghao Wang, Guangwei Luo and Rong Lu

Appl. Sci. 2021, 11(11), 4873; https://doi.org/10.3390/app11114873 - 26 May 2021

Cited by 3 | Viewed by 2451

Abstract

This study examines the usefulness of both geometric and volumetric measurements of orbital soft tissues on CT scans to provide quantitative diagnostic guidance in image reading of thyroid eye disease (TED). Computed tomography (CT) images were obtained from 92 orbits and were classified as impaired motility (TED-IM) and normal motility (TED-NM). The TED-IM group was further divided into dysthyroid optic neuropathy (DON) and non-DON groups. There were 5 volumetric, 2 angular, and 3 ratio parameter measurements acquired from CT images to examine their feasibility in TED classification. We found that the mean volumes of extraocular muscle and retroorbital fat and their ratio to the orbital volume were significantly different between the two motility groups. The mean ratio of extraocular muscle volume in orbital apex and orbital apex volume (EMV-OA/OAV) was significantly larger in DON than non-DON patients (p < 0.05). The population distribution among TED-NM, non-DON, and DON groups significantly varied for different angles between the optic nerve and medial rectus and lateral rectus. In conclusion, geometric and volumetric measurements using CT scans help to quantitatively classify TED. Full article

(This article belongs to the Special Issue Applications of Geometric Morphometrics and Computational Imaging)

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