WingSegment: A Computer Vision‐Based Hybrid Approach for Insect Wing Image Segmentation and 3D Printing

Abstract

This article introduces WingSegment, a MATLAB app‐designed tool employing a hybrid approach of computer vision and graph theory for precise insect wing image segmentation. WingSegment detects cells, junctions, Pterostigma, and venation patterns, measuring geometric features and generating Voronoi patterns. The tool utilizes region‐growing, thinning, and Dijkstra's algorithms for boundary detection, junction identification, and vein path extraction. It provides histograms and box plots of geometric features, facilitating comprehensive wing analysis. WingSegment's efficiency is validated through comparisons with established tools and manual measurements, demonstrating accurate results. The tool further enables exporting detected boundaries as FreeCAD macro files for 3D modeling and printing, supporting finite element analysis. Beyond advancing insect wing morphology understanding, WingSegment holds broader implications for diverse planar structures, including leaves and geocells. This tool not only enhances automated geometric analysis and 3D model generation in insect wing studies but also contributes to the broader advancement of analysis, 3D printing, and modeling technologies across various planar structures.

Allometric Scaling Reveals Evolutionary Constraint on Odonata Wing Cellularity via Critical Crack Length

“…All 389 wing images used in the study were obtained from the publication by Hoffmann et al [33] All images were edited to ensure no noise would influence the measurements. A MATLAB toolbox called WingSegment [34] was used, an improved version of the previously developed toolbox, WingGram. [28] WingSegment employed computer vision and mathematical techniques, including region growing, [30,31] thinning, [35] and line simplification [32] algorithms, to extract the geometric characteristics of cells.…”