Planar morphometry via functional shape data analysis and quasi-conformal mappings

2026-05-07

AI Summary

Existing planar morphometric methods struggle to simultaneously capture the coupling between shape boundaries and internal features and lack robust quantitative modeling of shape variation. This work proposes a unified framework that integrates boundary and interior information by performing elastic registration of closed curves using square-root velocity functions and extending boundary correspondences across the entire domain via quasiconformal mapping. Anatomical consistency is further enhanced through landmark constraints, enabling integrated quantitative analysis of shape deformation and variability. Experiments on leaf and insect wing datasets demonstrate that the proposed method significantly outperforms existing approaches relying solely on boundary or internal features, achieving more accurate characterization of complex morphological variations.

Abstract

The study of shapes is one of the most fundamental problems in life sciences. Although numerous methods have been developed for the morphometry of planar biological shapes over the past several decades, most of them focus solely on either the outer silhouettes or the interior features of the shapes without capturing the coupling between them. Moreover, many existing shape mapping techniques are limited to establishing correspondence between planar structures without further allowing for the quantitative analysis or modelling of shape changes. In this work, we introduce FDA-QC, a novel planar morphometry method that combines functional shape data analysis (FDA) techniques and quasi-conformal (QC) mappings, taking both the boundary and interior of the planar shapes into consideration. Specifically, closed planar curves are represented by their square-root velocity functions and registered by elastic matching in the function space. The induced boundary correspondence is then extended to the entire planar domains by a quasi-conformal map, optionally with landmark constraints. Moreover, the proposed FDA-QC method can naturally lead to a unified framework for shape morphing and shape variation quantification. We apply the FDA-QC method to various leaf and insect wing datasets, and the experimental results show that the proposed combined approach captures morphological variation more effectively than purely boundary-based or interior-based descriptions. Altogether, our work paves a new way for understanding the growth and form of planar biological shapes.

Problem

• planar morphometry
• shape correspondence
• morphological variation
• boundary-interior coupling
• shape analysis

Innovation

• functional shape data analysis
• quasi-conformal mapping
• planar morphometry
• shape correspondence
• elastic matching