ISCC Processing for Multi-Dimensional Bioimage Data

Generate ISO 24138:2024 International Standard Content Codes (ISCC) for bioimage data across multiple formats using deterministic IMAGEWALK plane traversal.

Version 0.1.0 - Unreleased.

iscc-bio bridges bioimage formats with ISCC-CODE processing by implementing the IMAGEWALK specification - a deterministic algorithm for traversing and canonicalizing pixel data from multi-dimensional bioimaging data. This produces consistent, reproducible content identifiers regardless of source format or storage platform.

Documentation: https://bio.iscc.codes

• Format-Agnostic Hashing: Generate reproducible ISCCs at the level of pixel data across OME-TIFF, OME-Zarr, OMERO, CZI, ND2, LIF, and other formats
• IMAGEWALK Implementation: Deterministic Z→C→T plane traversal with canonical byte representation
• Multi-Source Support: Process local files (via BioIO), OME-Zarr archives, and OMERO remote servers
• Memory Efficient: Lazy loading with Dask for processing large multi-dimensional images
• Multi-Scene Processing: Handle complex multi-scene/multi-series bioimage files
• Command-Line Tools: CLI commands for code generation, pixel hashing, and view extraction

# Using uv (recommended)
uv tool install iscc-bio

# Using pip
pip install iscc-bio

# Install with all bioimage reader plugins
uv tool install "iscc-bio[readers]"

# Install with specific format support
uv tool install "iscc-bio[czi,nd2,lif]"

# Install with OMERO support
uv tool install "iscc-bio[omero]"

# Install everything
uv tool install "iscc-bio[all]"

• readers: All BioIO reader plugins (BioFormats, CZI, OME-TIFF, OME-Zarr, ND2, LIF, etc.)
• omero: OMERO Blitz gateway for remote server access
• bioformats: BioFormats reader for broad format support
• czi, nd2, lif, ome-tiff, ome-zarr-plugin, dv, tifffile: Individual format readers

# Generate ISCC-based bioimage fingerprint
iscc-bio biocode myimage.czi

# Output includes:
# - ISCC-SUM hash over normalized pixel content
# - Representative view extraction (~5 views per scene)
# - ISCC-IMAGE codes for each view
# - ISCC-MIXED global descriptor

# Generate reproducible pixel hash using IMAGEWALK
iscc-bio pixhash myimage.ome.tiff

# Works with multiple sources:
iscc-bio pixhash local/file.czi           # Local bioimage file
iscc-bio pixhash data.zarr                # OME-Zarr/NGFF
iscc-bio pixhash --host omero.server.com --iid 123  # OMERO server

# Extract intelligent 2D views for perceptual hashing
iscc-bio views myimage.nd2 --output-dir ./views/

# Extraction strategies:
# - Maximum intensity projections (MIP)
# - Best focus planes
# - Representative sampling
# - Multi-channel composites

IMAGEWALK is a deterministic algorithm for traversing multi-dimensional bioimage data to produce format-agnostic, reproducible hash digests.

1. Z→C→T Traversal Order: Planes are processed in deterministic order:

   • Outermost loop: Z dimension (depth/focal plane)
   • Middle loop: C dimension (channel)
   • Innermost loop: T dimension (time)

2. Canonical Byte Representation: Each 2D plane is:

   • Flattened in row-major order (Y then X)
   • Encoded as big-endian bytes
   • Fed to a hash processor

3. Multi-Scene Independence: Each scene/series is processed separately, producing one hash per scene

For an image with Z=2, C=3, T=2 dimensions (12 total planes):

Plane 1:  z=0, c=0, t=0    Plane 7:  z=1, c=0, t=0
Plane 2:  z=0, c=0, t=1    Plane 8:  z=1, c=0, t=1
Plane 3:  z=0, c=1, t=0    Plane 9:  z=1, c=1, t=0
Plane 4:  z=0, c=1, t=1    Plane 10: z=1, c=1, t=1
Plane 5:  z=0, c=2, t=0    Plane 11: z=1, c=2, t=0
Plane 6:  z=0, c=2, t=1    Plane 12: z=1, c=2, t=1

• iw_bioio.py: BioIO-based implementation for local files
• iw_ngff.py: OME-NGFF/Zarr implementation using ome-zarr-py
• iw_blitz.py: OMERO Blitz implementation for remote servers

All implementations produce identical hashes for identical pixel data, conforming to the IMAGEWALK specification.

Create comprehensive bioimage fingerprints with ISCC codes:

iscc-bio biocode INPUT [OPTIONS]

Options:
  -o, --output-dir PATH    Save extracted view PNGs
  -n, --max-views INTEGER  Maximum views per scene (default: 5)

Generate reproducible SHA1 hashes over normalized pixel data:

iscc-bio pixhash INPUT [OPTIONS]

Options:
  -s, --source [auto|bioio|omero|zarr]  Data source type
  --host TEXT                           OMERO server hostname
  --iid INTEGER                         OMERO image ID

Extract intelligent 2D views for perceptual hashing:

iscc-bio views INPUT [OPTIONS]

Options:
  -s, --strategies TEXT    View strategies (mip, best_focus, representative, composite)
  -n, --max-views INTEGER  Maximum views to extract (default: 8)
  -o, --output-dir PATH    Directory to save thumbnails
  --host TEXT              OMERO server hostname
  --iid INTEGER            OMERO image ID

Extract thumbnails from all scenes in a multi-scene file:

iscc-bio scenes INPUT

Extract a single representative thumbnail from a bioimage:

iscc-bio thumb INPUT

from iscc_bio.imagewalk.iw_bioio import iter_planes_bioio
from iscc_bio.imagewalk.iw_ngff import iter_planes_ngff
from iscc_bio.imagewalk.iw_blitz import iter_planes_blitz

# Iterate over planes using BioIO
for plane in iter_planes_bioio("image.czi"):
    print(f"Scene {plane.scene_idx}, Z={plane.z_depth}, "
          f"C={plane.c_channel}, T={plane.t_time}")
    print(f"Shape: {plane.xy_array.shape}, dtype: {plane.xy_array.dtype}")

# Iterate over OME-Zarr planes
for plane in iter_planes_ngff("data.zarr"):
    # Process plane.xy_array (2D numpy array)
    pass

# Iterate over OMERO planes
from omero.gateway import BlitzGateway
conn = BlitzGateway("user", "pass", host="omero.server.com")
conn.connect()
image = conn.getObject("Image", 123)

for plane in iter_planes_blitz(image):
    # Process plane.xy_array
    pass
conn.close()

from iscc_bio.biocode import generate_biocode, format_output

# Generate bioimage fingerprints
fingerprints = generate_biocode("image.nd2", max_views=5)

# Format output
output = format_output(fingerprints, "image.nd2")
print(output)

from iscc_bio.pixhash import pixhash_bioio, pixhash_zarr, pixhash_omero

# Generate pixel hash (returns list of hashes, one per scene)
hashes = pixhash_bioio("image.lif")
print(hashes[0])  # Hash for first scene

# OME-Zarr
hashes = pixhash_zarr("data.zarr")

# OMERO
hashes = pixhash_omero("omero.server.com", image_id=123)

Via BioIO plugin ecosystem:

• OME-TIFF/TIFF: Multi-page TIFF with OME-XML metadata
• OME-Zarr/NGFF: Next-generation file format
• OMERO: Remote server access via Blitz gateway
• CZI: Carl Zeiss Image format
• ND2: Nikon NIS-Elements format
• LIF: Leica Image File format
• DV: DeltaVision format
• BioFormats: 150+ formats via Bio-Formats Java library

# Clone repository
git clone https://github.com/bio-codes/iscc-bio.git
cd iscc-bio

# Install with all dependencies
uv sync --extra all

# Run CLI during development
uv run iscc-bio --help

This project uses poethepoet for task automation:

# Format markdown files
uv run poe format-md

# Format code files
uv run poe format-code

# Build documentation
uv run poe docs-build

# Run all formatting and docs
uv run poe all

• iscc_bio.imagewalk: IMAGEWALK plane traversal implementations

  • iw_bioio.py: BioIO implementation
  • iw_ngff.py: OME-Zarr/NGFF implementation
  • iw_blitz.py: OMERO Blitz implementation
  • models.py: Plane data model

• iscc_bio.biocode: ISCC bioimage fingerprint generation

• iscc_bio.pixhash: Normalized pixel hashing across sources

• iscc_bio.views: Intelligent view extraction strategies

• iscc_bio.cli: Command-line interface

1. Lazy Loading: Uses Dask arrays for memory-efficient processing of large images
2. Format Agnostic: Identical processing logic across all formats via IMAGEWALK
3. Deterministic: Reproducible hashes across platforms and formats
4. Modular: Clean separation between traversal, canonicalization, and hashing

This work was supported through the Open Science Clusters’ Action for Research and Society (OSCARS) European project under grant agreement Nº101129751.

See: BIO-CODES project (Enhancing AI-Readiness of Bioimaging Data with Content-Based Identifiers).

Apache License 2.0 - See LICENSE file for details.

If you use iscc-bio in your research, please cite:

@software{iscc_bio,
  title        = {bio-codes/iscc-bio: ISCC Processing for Bioimage Data},
  author       = {Pan, Titusz},
  year         = 2025,
  url          = {https://github.com/bio-codes/iscc-bio},
  note         = {Supported by OSCARS (Open Science Clusters' Action for Research and Society) under European Commission grant agreement Nº101129751},
  version      = {0.1.0}
}

• iscc-sum - Fast ISCC Data-Code and Instance-Code hashing
• iscc-core - ISCC Core Algorithms
• BioIO - Bioimage reading library
• OME-Zarr - Next-generation file format implementation