gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/exploratory-data-analysis/references/microscopy_imaging_formats.md

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