DOI: 10.5061/dryad.37pvmcvp5
title: A high-throughput multispectral imaging system for museum specimens

description: We present an economical imaging system with integrated hardware and
 software to capture multispectral images of Lepidoptera with high
 efficiency. This method facilitates the comparison of colors and shapes
 among species at fine and broad taxonomic scales and may be adapted for
 other insect orders with greater three-dimensionality. Our system can
 image both the dorsal and ventral sides of pinned specimens. Together with
 our processing pipeline, the descriptive data can be used to
 systematically investigate multispectral colors and shapes based on
 full-wing reconstruction and a universally applicable ground plan that
 objectively quantifies wing patterns for species with different wing
 shapes (including tails) and venation systems. Basic morphological
 measurements, such as body length, thorax width, and antenna size are
 automatically generated. This system can increase exponentially the amount
 and quality of trait data extracted from museum specimens.
description: <strong>Processed
 data</strong> These data include but are not
 limited to all parameters generated during image processing, gridded
 multispectral reflectance, wing shapes, and the measurements of body size
 and antennae. The detailed data structure can be found on the GitHub
 repository.  <strong>Map of archived
 materials, protocols, and tutorials</strong> To
 prevent potential conflicts, scripts for different purposes on the cluster
 and on the local machine are provided in different protocols on
 Protocols.io and repositories on GitHub. Here, the summary of online
 protocols and source codes are organized as follows. Inclusion in
 [Protocol] indicates the corresponding step-by-step instruction on
 Protocols.io; inclusion in [Cluster] indicates the script will run better
 on the cluster; inclusion in [Local] indicates the script is designed for
 local machines with relatively low CPU and memory demands.  ●    Raw data: files described in the
 following format <strong>[[Folder/File name]]:
 descriptions</strong> ●       [[Methodology_imaging_records.csv]]: A file recording image names and the barcode of imaged specimens ●       [[Drawer_img_nef]]: Drawer images in RAW (*.NEF) format (total 35 images) o   Five set of images: Method_1-1_dorsal, Method_1-1_ventral, Method_1-2_dorsal, Method_1-2_ventral, Method_1-r_ventral (with a scale bar placed upside-down) ●       [[Drawer_img_tiff]]: Drawer images in linearized 16-bit (*.tiff) format (total 35 images) ●       [[manual_bounding_box_par]]: Manually corrected bounding boxes ●       [[spp_img_inspection]]: Specimen images for visualization (*.jpg) o   [[Problematic]]: Those problematic ones that need to be manually corrected ●       [[spp_img_reMask_tiff_done]]: Specimen images (*.tiff) after the mask correction ●       [[spp_first_level_product]]: The initial descriptive data or ‘first-level products’ (*AllBandsMask.mat). Find <strong>Methods</strong> for the detailed data structure ●       [[spp_RGB_Imgs]]: Images used for manual fore-and hindwing segmentation o   [[Seg_done]]: Done images (*.jpg) o   [[Segmented]]: The fore-and hindwing segmentation parameters (*.json) ●       [[spp_segmentation_analysis]]: Segmented images after inspection and manual correction o   [[wing_segmentation_img]]: The visualizations of image segmentation (*.jpg) o   [[wing_shape_morph-seg]]: The results of image segmentation (*morph-seg.mat) o   [[morphology_analysis_spp_preference_table_template.csv]]: A table generated according to the images in the ‘wing_segmentation_img’ folder, which is later used for inspection o   [[morphology_analysis_spp_preference_table.csv]]: The result after manual inspection, which records the condition of different body parts of a specimen o   [[reflectance_table]]: The reflectance data for all body parts of all specimens ●       [[spp_wing_grids_generation]]: Generate wing grids and processed data o   [[inspect_imgs]]: The visualization (*.jpg) of wing grids (no correction was needed in these results) o   [[spp_wing_parameters]]: Processed wing data. The original folder name is kept here. o   [[wing_matrix_visualization]]: The summarized multispectral reflectance (NIR [740], fNIR [940], F, FinRGB, PolDiff, UV, UVF, white, whitePol1, whitePol2) according to wing grids. ●       [[spp_second-level_product]]: The processed “second-level products”. (*_d-v_gridsPars*.mat). Find <strong>Methods</strong> for the detailed data structure ●       [[group_summary]]: The summary statistics for specified groups o   [[specimen_groups.csv]]: A table specifying groups o   [[specimen_groups_group_barcode_list.json]]: The group table in JSON format o   [[summary_matrices]]: The summary results according to the group table (*._summary.mat) o   [[summary_visualization]]: The summary visualization for each group (*.png) o   [[shp_tail_adv_vis]]: Replot wing shape and tails by scripts for advanced visualization (*.png) o   [[tail_summary_visualization]]: Replot tails by scripts for advanced visualization (*.png)   ●    Blueprints and materials (Fig. 6) [Protocol] https://www.protocols.io/private/2E2FB268F7AF11EBB05F0A58A9FEAC02   ●    Bash scripts and shell scripts running on the cluster [Cluster] https://github.com/weipingchan/Bash_scripts_methodology_paper   ●    Image preprocessing to derive initial descriptive data for museum archiving [Protocol] https://www.protocols.io/private/DEF29A74E44E11EB96DA0A58A9FEAC02 [Cluster] https://github.com/weipingchan/single_img_processing ●       Inspection and manual correction of specimen bounding box (Fig. 3d) [Local] https://github.com/weipingchan/Drawer_img_manual_define_bounding_boxes ●       Inspection and manual correction of mask for background removal (Fig. 9a) [Local] commercial painting software, such as Adobe Photoshop   ●    Data preparation and processing for color and shape quantification [Protocol] https://www.protocols.io/private/DEF29A74E44E11EB96DA0A58A9FEAC02 ●       Body-part segmentation (Fig. 9c panels at right) manually defined fore-hindwing segmentation data [Local] https://github.com/weipingchan/body-seg_distribute Segmentation [Cluster] https://github.com/weipingchan/basic_segmentation Inspection and manual correction of primary landmarks (Fig. 9b) [Local] https://github.com/weipingchan/manual_landmark_correction ●       Multispectral reflectance at wing-size level (as table format; Fig. 9d) [Protocol] https://www.protocols.io/private/F3292DF1FE0211EB878B0A58A9FEAC02 [Cluster] https://github.com/weipingchan/multispectral_reflectance_wing-size_level ●       Dorsal-ventral side analyses (Fig. 4) [Protocol] https://www.protocols.io/private/F3292DF1FE0211EB878B0A58A9FEAC02 [Cluster] https://github.com/weipingchan/dorsal_ventral_analysis ●       Inspection and manual correction of secondary landmarks (Fig. 1d) [Local] https://github.com/weipingchan/manual_wing_grid_correction   ●    Visualization (Fig. 1g-h & Fig.5) [Protocol] https://www.protocols.io/private/F3292DF1FE0211EB878B0A58A9FEAC02 ●       Multispectral reflectance at wing-pattern level with wing shape summary [Local] https://github.com/weipingchan/dorsal_ventral_summary ●       Advanced visualization for wing shapes and tails (<strong>Methods</strong>) [Local] https://github.com/weipingchan/replot_tail_and_avg_shapes
description: The pipeline was mainly developed under Matlab and R, but the
 data formats (e.g. *.mat, *.json) can still be operated in Python or other
 interface.