BioImage Convert: Command-Line Interface Reference The imgcnv command-line utility is a powerful tool designed for the conversion, processing, and metadata extraction of multi-dimensional biomedical images. It supports a wide array of formats, including proprietary microscope formats (such as Carl Zeiss LSM, Leica LIF, and Olympus OIB) and standard scientific formats (like OME-TIFF and DICOM).
This reference guide provides the syntax, essential commands, and practical examples for integrating imgcnv into automated image processing pipelines. Basic Command Syntax
The general syntax for the imgcnv command-line tool follows this structure:
imgcnv -i Use code with caution. Core Arguments
-i : Specifies the path to the input image file or directory.
-o : Specifies the path and name of the output file.
-t : Defines the target output format (e.g., tiff, ome-tiff, jpeg, png, bwt). Common Format Identifiers
When using the -t flag, specify one of the following common format strings: Description tiff Standard Tagged Image File Format .tif, .tiff ome-tiff OME-TIFF (Open Microscopy Environment) .ome.tif png Portable Network Graphics (8-bit/16-bit) .png jpeg Joint Photographic Experts Group (8-bit lossy) .jpg klb Keller Lab Block compressed format .klb Meta-Data Extraction
Extracting metadata is often the first step in a bioimaging pipeline to understand pixel dimensions, channel counts, and spatial resolution. Print Metadata to Console
To view all header and geometry information without writing a new image file: imgcnv -i input_image.lsm -meta Use code with caution. Export Metadata to XML
To save the structured OME-XML metadata into an external file: imgcnv -i input_image.czi -meta-xml > metadata.xml Use code with caution. Image Transformation and Conversion Options
imgcnv allows modifications to the spatial and dimensional characteristics of images during the conversion process. 1. Dimensional Extraction (Slicing)
Biomedical images often contain 5 dimensions: X, Y, Z (slices), C (channels), and T (time points). You can isolate specific sub-volumes.
Extract specific channel: Extract only the second channel (index 1). imgcnv -i input.lif -o channel1.tif -t tiff -ch 1 Use code with caution. Extract Z-slice range: Extract slices 10 through 20. imgcnv -i input.oib -o z_range.tif -t tiff -z 10:20 Use code with caution. 2. Spatial Resizing and Pixel Scaling
Resize Dimensions: Resize the image to specific X and Y pixel dimensions.
imgcnv -i large_image.tif -o small_image.png -t png -resize 512,512 Use code with caution.
Force Resolution: Override or inject pixel resolution metadata (in microns).
imgcnv -i input.raw -o fixed.tif -t tiff -resolution 0.25,0.25,1.0 Use code with caution. 3. Intensity Scaling and Bit-Depth Conversion
Demote 16-bit to 8-bit: Convert a high-dynamic-range image to an 8-bit format suitable for standard displays, using auto-scaling.
imgcnv -i 16bit_input.nd2 -o 8bit_output.jpg -t jpeg -depth 8 -display-auto Use code with caution. Advanced Pipeline Examples
Example 1: Convert a 3D Stack to a 2D Maximum Intensity Projection
To project a 3D Z-stack into a single 2D image by keeping the brightest pixels along the Z-axis:
imgcnv -i brain_stack.lsm -o projection.tif -t tiff -project max Use code with caution. Example 2: Batch Processing via Shell Loop
To convert a directory full of proprietary .czi files into standard compressed .tif files using a bash loop:
for file in.czi; do imgcnv -i “\(file" -o "\){file%.czi}.tif” -t tiff done Use code with caution. Error Handling and Troubleshooting
Format Not Supported: If imgcnv fails to recognise an input file, verify the format using imgcnv -formats to list all compiled image readers and writers.
Memory Issues: Multi-gigabyte tiles or time-series can cause memory exhaustion. Use the -tile option if supported by your build to process images in smaller blocks.
To help tailor this guide or troubleshoot a specific workflow, let me know:
What input image format (e.g., CZI, LIF, LSM) you are currently working with?
Leave a Reply