Shape-changing chains for morphometric analysis of 2D and 3D, open or closed outlines

Li, Bingjue; Zhou, Shengmin; Murray, Andrew P.; Subsol, Gérard

doi:10.1038/s41598-021-00911-5

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…The findings of both studies suggest that the GM analysis can be a useful tool for identifying morphological similarities between populations. Li et al (2021) provided a methodology to synthesise a shape-changing chain for 2D curve fitting and employed the chain parameters in stepwise discriminant analysis, which was shaown to be applicable to a wide range of morphometric problems involving 2D/3D, open/closed outlines 3 .…”

Section: Introductionmentioning

confidence: 99%

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

Pillay,

Pathmanathan,

Dabo-Niang

et al. 2023

Preprint

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This work proposes a functional data analysis (FDA) approach for morphometrics in classifying three shrew species (S. murinus, C. monticola and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 90 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear models) using predicted PC scores obtained from both methods (combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species. Overall, the generalised linear models (GLM) was the most accurate (95.4% accuracy) among the four classification models.

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Section: Introductionmentioning

confidence: 99%

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

Pillay,

Pathmanathan,

Dabo-Niang

et al. 2023

Preprint

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A novel method for assessment of human midpalatal sutures using CBCT-based geometric morphometrics and complexity scores

et al. 2023

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Introduction Management of dentofacial deficiencies requires knowledge about sutural morphology and complexity. The present study assesses midpalatal sutural morphology based on human cone-beam computed tomography (CBCT) using geometric morphometrics (GMM) and complexity scores. The study is the first to apply a sutural complexity score to human CBCT datasets and demonstrates the potential such a score has to improve objectiveness and comparability when analysing the midpalatal suture. Materials and methods CBCTs of various age and sex groups were analysed retrospectively (n = 48). For the geometric morphometric analysis, landmark acquisition and generalised Procrustes superimposition were combined with principal component analysis to detect variability in sutural shape patterns. For complexity analysis, a windowed short-time Fourier transform with a power spectrum density (PSD) calculation was applied to resampled superimposed semi-landmarks. Results According to the GMM, younger patients exhibited comparable sutural patterns. With increasing age, the shape variation increased among the samples. The principal components did not sufficiently capture complexity patterns, so an additional methodology was applied to assess characteristics such as sutural interdigitation. According to the complexity analysis, the average PSD complexity score was 1.465 (standard deviation = 0.010). Suture complexity increased with patient age (p < 0.0001), but was not influenced by sex (p = 0.588). The intra-class correlation coefficient exceeded 0.9, indicating intra-rater reliability. Conclusion Our study demonstrated that GMM applied to human CBCTs can reveal shape variations and allow the comparison of sutural morphologies across samples. We demonstrate that complexity scores can be applied to study human sutures captured in CBCTs and complement GMM for a comprehensive sutural analysis.

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Objective Analysis of Orbital Rim Fracture CT Images Using Curve and Area Measurement

Lee,

Yoo,

Kim

et al. 2024

Preprint

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The orbital bone presents a closed curve, and fracture results in disfigurement. An image analysis procedure was developed to examine before and after corrective surgery. An ellipse and circumscribed contour embodied the closed curve. Three-dimensional (3D) computed tomography (CT) images of were collected from 25 patients. Orbital rim data were generated, and binary images were created to facilitate closed curve analysis. Various indices, including the solidity value (closed curve area/convex hull area) and ellipse distance (discrepancy between the closed curve and the ellipse traversing the curve), were utilized. The ratios of various indices—including the number of vertices, solidity value, and ellipse distance—between the affected and unaffected sides showed postoperative values that were closer to 1, which would indicate perfect symmetry, than the preoperative measurements (P < 0.05). The solidity value increased, while both the ellipse distance and curvature values decreased, reflecting the transformation of bends into smooth contours following reduction surgery (P < 0.05). Significant correlations were observed between 1-solidity, ellipse distance, and curvature using the Pearson correlation test (P < 0.05). This study validated postoperative changes in various indices and established correlations among multiple values, specifically solidity, ellipse distance, and curvature. Employing multiple indices with mutual complements has provided objective information confidently.

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Shape-changing chains for morphometric analysis of 2D and 3D, open or closed outlines

Cited by 3 publications

References 27 publications

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

A novel method for assessment of human midpalatal sutures using CBCT-based geometric morphometrics and complexity scores

Objective Analysis of Orbital Rim Fracture CT Images Using Curve and Area Measurement

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