Microscopy and Imaging File Formats Reference

This reference covers file formats used in microscopy, medical imaging, remote sensing, and scientific image analysis.

Microscopy-Specific Formats

.tif / .tiff - Tagged Image File Format

Description: Flexible image format supporting multiple pages and metadata Typical Data: Microscopy images, z-stacks, time series, multi-channel Use Cases: Fluorescence microscopy, confocal imaging, biological imaging Python Libraries:

tifffile: tifffile.imread('file.tif') - Microscopy TIFF support
PIL/Pillow: Image.open('file.tif') - Basic TIFF
scikit-image: io.imread('file.tif')
AICSImageIO: Multi-format microscopy reader EDA Approach:
Image dimensions and bit depth
Multi-page/z-stack analysis
Metadata extraction (OME-TIFF)
Channel analysis and intensity distributions
Temporal dynamics (time-lapse)
Pixel size and spatial calibration
Histogram analysis per channel
Dynamic range utilization

.nd2 - Nikon NIS-Elements

Description: Proprietary Nikon microscope format Typical Data: Multi-dimensional microscopy (XYZCT) Use Cases: Nikon microscope data, confocal, widefield Python Libraries:

nd2reader: ND2Reader('file.nd2')
pims: pims.ND2_Reader('file.nd2')
AICSImageIO: Universal reader EDA Approach:
Experiment metadata extraction
Channel configurations
Time-lapse frame analysis
Z-stack depth and spacing
XY stage positions
Laser settings and power
Pixel binning information
Acquisition timestamps

.lif - Leica Image Format

Description: Leica microscope proprietary format Typical Data: Multi-experiment, multi-dimensional images Use Cases: Leica confocal and widefield data Python Libraries:

readlif: readlif.LifFile('file.lif')
AICSImageIO: LIF support
python-bioformats: Via Bio-Formats EDA Approach:
Multiple experiment detection
Image series enumeration
Metadata per experiment
Channel and timepoint structure
Physical dimensions extraction
Objective and detector information
Scan settings analysis

.czi - Carl Zeiss Image

Description: Zeiss microscope format Typical Data: Multi-dimensional microscopy with rich metadata Use Cases: Zeiss confocal, lightsheet, widefield Python Libraries:

czifile: czifile.CziFile('file.czi')
AICSImageIO: CZI support
pylibCZIrw: Official Zeiss library EDA Approach:
Scene and position analysis
Mosaic tile structure
Channel wavelength information
Acquisition mode detection
Scaling and calibration
Instrument configuration
ROI definitions

.oib / .oif - Olympus Image Format

Description: Olympus microscope formats Typical Data: Confocal and multiphoton imaging Use Cases: Olympus FluoView data Python Libraries:

AICSImageIO: OIB/OIF support
python-bioformats: Via Bio-Formats EDA Approach:
Directory structure validation (OIF)
Metadata file parsing
Channel configuration
Scan parameters
Objective and filter information
PMT settings

.vsi - Olympus VSI

Description: Olympus slide scanner format Typical Data: Whole slide imaging, large mosaics Use Cases: Virtual microscopy, pathology Python Libraries:

openslide-python: openslide.OpenSlide('file.vsi')
AICSImageIO: VSI support EDA Approach:
Pyramid level analysis
Tile structure and overlap
Macro and label images
Magnification levels
Whole slide statistics
Region detection

.ims - Imaris Format

Description: Bitplane Imaris HDF5-based format Typical Data: Large 3D/4D microscopy datasets Use Cases: 3D rendering, time-lapse analysis Python Libraries:

h5py: Direct HDF5 access
imaris_ims_file_reader: Specialized reader EDA Approach:
Resolution level analysis
Time point structure
Channel organization
Dataset hierarchy
Thumbnail generation
Memory-mapped access strategies
Chunking optimization

.lsm - Zeiss LSM

Description: Legacy Zeiss confocal format Typical Data: Confocal laser scanning microscopy Use Cases: Older Zeiss confocal data Python Libraries:

tifffile: LSM support (TIFF-based)
python-bioformats: LSM reading EDA Approach:
Similar to TIFF with LSM-specific metadata
Scan speed and resolution
Laser lines and power
Detector gain and offset
LUT information

.stk - MetaMorph Stack

Description: MetaMorph image stack format Typical Data: Time-lapse or z-stack sequences Use Cases: MetaMorph software output Python Libraries:

tifffile: STK is TIFF-based
python-bioformats: STK support EDA Approach:
Stack dimensionality
Plane metadata
Timing information
Stage positions
UIC tags parsing

.dv - DeltaVision

Description: Applied Precision DeltaVision format Typical Data: Deconvolution microscopy Use Cases: DeltaVision microscope data Python Libraries:

mrc: Can read DV (MRC-related)
AICSImageIO: DV support EDA Approach:
Wave information (channels)
Extended header analysis
Lens and magnification
Deconvolution status
Time stamps per section

.mrc - Medical Research Council

Description: Electron microscopy format Typical Data: EM images, cryo-EM, tomography Use Cases: Structural biology, electron microscopy Python Libraries:

mrcfile: mrcfile.open('file.mrc')
EMAN2: EM-specific tools EDA Approach:
Volume dimensions
Voxel size and units
Origin and map statistics
Symmetry information
Extended header analysis
Density statistics
Header consistency validation

.dm3 / .dm4 - Gatan Digital Micrograph

Description: Gatan TEM/STEM format Typical Data: Transmission electron microscopy Use Cases: TEM imaging and analysis Python Libraries:

hyperspy: hs.load('file.dm3')
ncempy: ncempy.io.dm.dmReader('file.dm3') EDA Approach:
Microscope parameters
Energy dispersive spectroscopy data
Diffraction patterns
Calibration information
Tag structure analysis
Image series handling

.eer - Electron Event Representation

Description: Direct electron detector format Typical Data: Electron counting data from detectors Use Cases: Cryo-EM data collection Python Libraries:

mrcfile: Some EER support
Vendor-specific tools (Gatan, TFS) EDA Approach:
Event counting statistics
Frame rate and dose
Detector configuration
Motion correction assessment
Gain reference validation

.ser - TIA Series

Description: FEI/TFS TIA format Typical Data: EM image series Use Cases: FEI/Thermo Fisher EM data Python Libraries:

hyperspy: SER support
ncempy: TIA reader EDA Approach:
Series structure
Calibration data
Acquisition metadata
Time stamps
Multi-dimensional data organization

Medical and Biological Imaging

.dcm - DICOM

Description: Digital Imaging and Communications in Medicine Typical Data: Medical images with patient/study metadata Use Cases: Clinical imaging, radiology, CT, MRI, PET Python Libraries:

pydicom: pydicom.dcmread('file.dcm')
SimpleITK: sitk.ReadImage('file.dcm')
nibabel: Limited DICOM support EDA Approach:
Patient metadata extraction (anonymization check)
Modality-specific analysis
Series and study organization
Slice thickness and spacing
Window/level settings
Hounsfield units (CT)
Image orientation and position
Multi-frame analysis

.nii / .nii.gz - NIfTI

Description: Neuroimaging Informatics Technology Initiative Typical Data: Brain imaging, fMRI, structural MRI Use Cases: Neuroimaging research, brain analysis Python Libraries:

nibabel: nibabel.load('file.nii')
nilearn: Neuroimaging with ML
SimpleITK: NIfTI support EDA Approach:
Volume dimensions and voxel size
Affine transformation matrix
Time series analysis (fMRI)
Intensity distribution
Brain extraction quality
Registration assessment
Orientation validation
Header information consistency

.mnc - MINC Format

Description: Medical Image NetCDF Typical Data: Medical imaging (predecessor to NIfTI) Use Cases: Legacy neuroimaging data Python Libraries:

pyminc: MINC-specific tools
nibabel: MINC support EDA Approach:
Similar to NIfTI
NetCDF structure exploration
Dimension ordering
Metadata extraction

.nrrd - Nearly Raw Raster Data

Description: Medical imaging format with detached header Typical Data: Medical images, research imaging Use Cases: 3D Slicer, ITK-based applications Python Libraries:

pynrrd: nrrd.read('file.nrrd')
SimpleITK: NRRD support EDA Approach:
Header field analysis
Encoding format
Dimension and spacing
Orientation matrix
Compression assessment
Endianness handling

.mha / .mhd - MetaImage

Description: MetaImage format (ITK) Typical Data: Medical/scientific 3D images Use Cases: ITK/SimpleITK applications Python Libraries:

SimpleITK: Native MHA/MHD support
itk: Direct ITK integration EDA Approach:
Header-data file pairing (MHD)
Transform matrix
Element spacing
Compression format
Data type and dimensions

.hdr / .img - Analyze Format

Description: Legacy medical imaging format Typical Data: Brain imaging (pre-NIfTI) Use Cases: Old neuroimaging datasets Python Libraries:

nibabel: Analyze support
Conversion to NIfTI recommended EDA Approach:
Header-image pairing validation
Byte order issues
Conversion to modern formats
Metadata limitations

Scientific Image Formats

.png - Portable Network Graphics

Description: Lossless compressed image format Typical Data: 2D images, screenshots, processed data Use Cases: Publication figures, lossless storage Python Libraries:

PIL/Pillow: Image.open('file.png')
scikit-image: io.imread('file.png')
imageio: imageio.imread('file.png') EDA Approach:
Bit depth analysis (8-bit, 16-bit)
Color mode (grayscale, RGB, palette)
Metadata (PNG chunks)
Transparency handling
Compression efficiency
Histogram analysis

.jpg / .jpeg - Joint Photographic Experts Group

Description: Lossy compressed image format Typical Data: Natural images, photos Use Cases: Visualization, web graphics (not raw data) Python Libraries:

PIL/Pillow: Standard JPEG support
scikit-image: JPEG reading EDA Approach:
Compression artifacts detection
Quality factor estimation
Color space (RGB, grayscale)
EXIF metadata
Quantization table analysis
Note: Not suitable for quantitative analysis

.bmp - Bitmap Image

Description: Uncompressed raster image Typical Data: Simple images, screenshots Use Cases: Compatibility, simple storage Python Libraries:

PIL/Pillow: BMP support
scikit-image: BMP reading EDA Approach:
Color depth
Palette analysis (if indexed)
File size efficiency
Pixel format validation

.gif - Graphics Interchange Format

Description: Image format with animation support Typical Data: Animated images, simple graphics Use Cases: Animations, time-lapse visualization Python Libraries:

PIL/Pillow: GIF support
imageio: Better GIF animation support EDA Approach:
Frame count and timing
Palette limitations (256 colors)
Loop count
Disposal method
Transparency handling

.svg - Scalable Vector Graphics

Description: XML-based vector graphics Typical Data: Vector drawings, plots, diagrams Use Cases: Publication-quality figures, plots Python Libraries:

svgpathtools: Path manipulation
cairosvg: Rasterization
lxml: XML parsing EDA Approach:
Element structure analysis
Style information
Viewbox and dimensions
Path complexity
Text element extraction
Layer organization

.eps - Encapsulated PostScript

Description: Vector graphics format Typical Data: Publication figures Use Cases: Legacy publication graphics Python Libraries:

PIL/Pillow: Basic EPS rasterization
ghostscript via subprocess EDA Approach:
Bounding box information
Preview image validation
Font embedding
Conversion to modern formats

.pdf (Images)

Description: Portable Document Format with images Typical Data: Publication figures, multi-page documents Use Cases: Publication, data presentation Python Libraries:

PyMuPDF/fitz: fitz.open('file.pdf')
pdf2image: Rasterization
pdfplumber: Text and layout extraction EDA Approach:
Page count
Image extraction
Resolution and DPI
Embedded fonts and metadata
Compression methods
Image vs vector content

.fig - MATLAB Figure

Description: MATLAB figure file Typical Data: MATLAB plots and figures Use Cases: MATLAB data visualization Python Libraries:

Custom parsers (MAT file structure)
Conversion to other formats EDA Approach:
Figure structure
Data extraction from plots
Axes and label information
Plot type identification

.hdf5 (Imaging Specific)

Description: HDF5 for large imaging datasets Typical Data: High-content screening, large microscopy Use Cases: BigDataViewer, large-scale imaging Python Libraries:

h5py: Universal HDF5 access
Imaging-specific readers (BigDataViewer) EDA Approach:
Dataset hierarchy
Chunk and compression strategy
Multi-resolution pyramid
Metadata organization
Memory-mapped access
Parallel I/O performance

.zarr - Chunked Array Storage

Description: Cloud-optimized array storage Typical Data: Large imaging datasets, OME-ZARR Use Cases: Cloud microscopy, large-scale analysis Python Libraries:

zarr: zarr.open('file.zarr')
ome-zarr-py: OME-ZARR support EDA Approach:
Chunk size optimization
Compression codec analysis
Multi-scale representation
Array dimensions and dtype
Metadata structure (OME)
Cloud access patterns

.raw - Raw Image Data

Description: Unformatted binary pixel data Typical Data: Raw detector output Use Cases: Custom imaging systems Python Libraries:

numpy: np.fromfile() with dtype
imageio: Raw format plugins EDA Approach:
Dimensions determination (external info needed)
Byte order and data type
Header presence detection
Pixel value range
Noise characteristics

.bin - Binary Image Data

Description: Generic binary image format Typical Data: Raw or custom-formatted images Use Cases: Instrument-specific outputs Python Libraries:

numpy: Custom binary reading
struct: For structured binary data EDA Approach:
Format specification required
Header parsing (if present)
Data type inference
Dimension extraction
Validation with known parameters

Image Analysis Formats

.roi - ImageJ ROI

Description: ImageJ region of interest format Typical Data: Geometric ROIs, selections Use Cases: ImageJ/Fiji analysis workflows Python Libraries:

read-roi: read_roi.read_roi_file('file.roi')
roifile: ROI manipulation EDA Approach:
ROI type analysis (rectangle, polygon, etc.)
Coordinate extraction
ROI properties (area, perimeter)
Group analysis (ROI sets)
Z-position and time information

.zip (ROI sets)

Description: ZIP archive of ImageJ ROIs Typical Data: Multiple ROI files Use Cases: Batch ROI analysis Python Libraries:

read-roi: read_roi.read_roi_zip('file.zip')
Standard zipfile module EDA Approach:
ROI count in set
ROI type distribution
Spatial distribution
Overlapping ROI detection
Naming conventions

.ome.tif / .ome.tiff - OME-TIFF

Description: TIFF with OME-XML metadata Typical Data: Standardized microscopy with rich metadata Use Cases: Bio-Formats compatible storage Python Libraries:

tifffile: OME-TIFF support
AICSImageIO: OME reading
python-bioformats: Bio-Formats integration EDA Approach:
OME-XML validation
Physical dimensions extraction
Channel naming and wavelengths
Plane positions (Z, C, T)
Instrument metadata
Bio-Formats compatibility

.ome.zarr - OME-ZARR

Description: OME-NGFF specification on ZARR Typical Data: Next-generation file format for bioimaging Use Cases: Cloud-native imaging, large datasets Python Libraries:

ome-zarr-py: Official implementation
zarr: Underlying array storage EDA Approach:
Multiscale resolution levels
Metadata compliance with OME-NGFF spec
Coordinate transformations
Label and ROI handling
Cloud storage optimization
Chunk access patterns

.klb - Keller Lab Block

Description: Fast microscopy format for large data Typical Data: Lightsheet microscopy, time-lapse Use Cases: High-throughput imaging Python Libraries:

pyklb: KLB reading and writing EDA Approach:
Compression efficiency
Block structure
Multi-resolution support
Read performance benchmarking
Metadata extraction

.vsi - Whole Slide Imaging

Description: Virtual slide format (multiple vendors) Typical Data: Pathology slides, large mosaics Use Cases: Digital pathology Python Libraries:

openslide-python: Multi-format WSI
tiffslide: Pure Python alternative EDA Approach:
Pyramid level count
Downsampling factors
Associated images (macro, label)
Tile size and overlap
MPP (microns per pixel)
Background detection
Tissue segmentation

.ndpi - Hamamatsu NanoZoomer

Description: Hamamatsu slide scanner format Typical Data: Whole slide pathology images Use Cases: Digital pathology workflows Python Libraries:

openslide-python: NDPI support EDA Approach:
Multi-resolution pyramid
Lens and objective information
Scan area and magnification
Focal plane information
Tissue detection

.svs - Aperio ScanScope

Description: Aperio whole slide format Typical Data: Digital pathology slides Use Cases: Pathology image analysis Python Libraries:

openslide-python: SVS support EDA Approach:
Pyramid structure
MPP calibration
Label and macro images
Compression quality
Thumbnail generation

.scn - Leica SCN

Description: Leica slide scanner format Typical Data: Whole slide imaging Use Cases: Digital pathology Python Libraries:

openslide-python: SCN support EDA Approach:
Tile structure analysis
Collection organization
Metadata extraction
Magnification levels