rodfinding

Rodfinding is a computational technique used to detect and localize rod-shaped objects in images or volumetric data. The term is used in fields such as microbiology and nanomaterials to identify elongated, cylindrical structures—bacterial bacilli or nanorods—and to quantify their properties, including position, orientation, length, and density.

Approaches combine traditional image processing with modern machine learning. Classical methods rely on thresholding, edge enhancement, and morphological filters to emphasize elongated features, followed by shape analysis using aspect ratio, elongation, and skeletonization. The Hough transform and its variants are commonly used to detect straight or gently curved rods. In contemporary workflows, convolutional neural networks trained on annotated images perform detection and instance segmentation of individual rods, with post-processing to separate touching or clustered objects and to estimate orientation.

Applications span microbiology, materials science, and quality control. In microbiology, rodfinding supports counting and sizing populations of bacilli in culture or tissue samples. In materials science, it helps quantify nanorod distributions in electron microscopy images, informing synthesis and quality assessment. In manufacturing and pharmaceutical research, rodfinding underpins particle sizing and dispersion studies that influence formulation and delivery properties.

Challenges include overlapping rods, variable contrast and noise, bent or irregular rods, and occlusions in three-dimensional data. While 2D methods are common, 3D imaging and multi-view reconstruction offer improved accuracy for thick samples. The term itself is a descriptive label in image-analysis communities rather than a formal standard.