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+# 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