Image data formats

Prerequisites
Before starting this lesson, you should be familiar with:

Basic properties of images and pixels

Data types

Image calibration

Learning Objectives
After completing this lesson, learners should be able to:

Open various image files formats

Understand the difference between image data and metadata

Motivation

There are numerous ways how to save image data on disk. Virtually every microscope vendor has their own file format. It is thus very important to understand how to open those files and inspect their content. Moreover, some software will open only specific image file formats and thus it is sometime necessary to re-save the data. During such image file format conversions information can be lost; it is important to be aware of this and avoid such information loss as much as possible.

Concept map

graph TD F("TIFF, JPEG, XML/HDF5, CZI, LIF, ...") F --> PD("Pixel data") PD --> Values PD --> Dimensions F --> MD("Metadata") MD --> IC("Image calibration") MD --> MS("Microscope settings") MD --> DS("Display settings") MD --> NA("...")

Figure

Image pixel data are saved as binary data on disk. Essential metadata is needed to load the binary data into an image array.

Activities

Open TIFF image data

Open a TIFF (Tag Image File) file and inspect its pixel and metatdata content
Observe that there are different variants of TIFF files
Observe that reading the metadata correctly does not always work in the same way for the different variants

Data

Show activity for:

ImageJ GUI

Inspect the ImageJ-TIFF image

Open by drag and drop into the FIJI window:

Inspect the image

Inspect the metadata via [Image > Show info…]

Close the image and open it again via Bio-Formats Importer:

[Plugins > Bio-Formats > Bio-Formats Importer]

Select your image

Display metadata

Click [OK]

Both options should yield the same pixel calibration

Inspect the Molecular Devices TIFF image

Repeat the above steps and observe that the pixel calibration is only read by the Bio-Formats importer

Key points

There are different TIFF variants

There are different TIFF openers in Fiji

Typically, the Bio-Formats plugin does the best job in terms of reading all metadata

python BioIO

# %% 
# Open a tif image file
# minimal conda env for this module
# conda create -n ImageFileFormats python=3.10
# activate ImageFileFormat
# pip install bioio bioio-tifffile notebook


# %%
# Load .tif file
# - Observe that BioImage chooses the correct reader plugin
from bioio import BioImage
image_url = 'https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_PLK1_control.tif'
bioimage = BioImage(image_url)
print(bioimage)
print(type(bioimage))

# %%
# Inspect dimension and shape of image
print(f'Image dimension: {bioimage.dims}')
print(f'Dimension order is: {bioimage.dims.order}')
print(f'Image shape: {bioimage.shape}')

# %%
# Extract image data (5D)
image_data = bioimage.data
print(f'Image type: {type(image_data)}')
print(f'Image array shape: {image_data.shape}')
# Extract specific image part
image_data = bioimage.get_image_data('YX')
print(f'Image type: {type(image_data)}')
print(f'Image array shape: {image_data.shape}')

# %%
# Read pixel size
print(f'Pixel size: {bioimage.physical_pixel_sizes}')
# Read metadata
print(f'Metadata type: {type(bioimage.metadata)}')
print(f'Metadata: {bioimage.metadata}')

# %%
# Load .tif file with extensive metadata
image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_16bit__collagen.md.tif"
bioimage = BioImage(image_url)

# %%
# Read pixel size
print(f'Pixel size: {bioimage.physical_pixel_sizes}')
# Read metadata
print(f'Metadata type: {type(bioimage.metadata)}')
print(f'Metadata: {bioimage.metadata}')

Open CZI image data

Open a CZI (Carl Zeiss Image) file and inspect its pixel and metatdata content
Observe the data is presented as one file on disk, but contains two image datasets with the same dimensions

Data

CZI image file

Show activity for:

ImageJ GUI

Open the file mentioned in the activity using:

[Plugins > Bio-Formats > Bio-Format Importer]

Display metadata

Display OME-XML Metadata

Press [OK]

Select both “Series”

Look at the images

Inspect the metadata

Note that opening the CZI file via drag and drop also triggers the Bio-Formats plugin; this is not always the case(!); this is only true if ImageJ itself does not want to open the image itself (see the above activity about TIFF files where this can become confusing).

python BioIO

# %% 
# Open a CZI image file
# minimal conda env for this module
# conda create -n ImageFileFormats python=3.10
# activate ImageFileFormat
# pip install bioio bioio-tifffile bioio-lif bioio-czi bioio-ome-tiff bioio-ome-zarr notebook
# Note: for only dealing with .czi just do pip install bioio bioio-czi


# %%
# Load .czi file
# file needs first to be downloaded from https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz__multiple_images.czi
# save file in the same directory as this notebook
# - Observe that BioImage chooses the correct reader plugin
from bioio import BioImage
from pathlib import Path
bioimage = BioImage(Path().cwd()/'ExampleImages/xyz__multiple_images.czi')
print(bioimage)
print(type(bioimage))

# %%
# Inspect number of images in object
print(bioimage.scenes)

# %%
# Inspect both images in the object
for image in bioimage.scenes:
    print(f'Image name: {image}')
    # Select image:
    bioimage.set_scene(image)
    # Inspect dimension and shape of image
    print(f'Image dimension: {bioimage.dims}')
    print(f'Dimension order is: {bioimage.dims.order}')
    print(f'Image shape: {bioimage.shape}')
    # Extract image data (5D)
    image_data = bioimage.data
    print(f'Image type: {type(image_data)}')
    print(f'Image array shape: {image_data.shape}')
    # Extract specific image part
    image_data = bioimage.get_image_data('YX')
    print(f'Image type: {type(image_data)}')
    print(f'Image array shape: {image_data.shape}')
    # Read pixel size
    print(f'Pixel size: {bioimage.physical_pixel_sizes}')
    # Read metadata
    print(f'Metadata type: {type(bioimage.metadata)}')
    print(f'Metadata: {bioimage.metadata}')
    print('\n')

#%%
# Handle metadata
import xml.etree.ElementTree as ET
from xml.dom import minidom
# print a pretty version of the xml metadata
md = bioimage.metadata
xml_string = ET.tostring(md, encoding='unicode')
pretty_xml = minidom.parseString(xml_string).toprettyxml(indent="  ")
print(pretty_xml)

#%%
# Grab specific information
size_x = md.findtext(".//Image/SizeX")
pixel_type = md.findtext(".//Image/PixelType")
excitation_wavelength = md.findtext(".//Channel/ExcitationWavelength")
emission_wavelength = md.findtext(".//Channel/EmissionWavelength")
print(f'Image size in X: {size_x}')
print(f'Pixel type: {pixel_type}')
print(f'Excitation wavelength: {excitation_wavelength}')
print(f'Emission wavelength: {emission_wavelength}')

Open Leica LIF image data

Open a LIF (Leica Image File) file and inspect its pixel and metatdata content
Observe the data is presented as one file on disk, but the file contains two image data sets with different dimensions

Data

LIF image file

Show activity for:

ImageJ GUI

Open the file mentioned in the activity using:

[Plugins > Bio-Formats > Bio-Formats Importer]

Open all series (you can check this to open all series automatically)

Colormode: Composite

Display metadata

Display OME-XML Metadata

Press [OK]

Select both series

Look at the images

Inspect the different metadata for these two images.

python BioIO

# %% 
# Open a LIF image file
# minimal conda env for this module
# conda create -n ImageFileFormats python=3.10
# activate ImageFileFormat
# pip install bioio bioio-tifffile bioio-lif bioio-czi bioio-ome-tiff bioio-ome-zarr notebook
# Note: for only dealing with .lif just do pip install bioio bioio-lif


# %%
# Load .lif file
# - Observe that BioImage chooses the correct reader plugin
from bioio import BioImage
image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_xyc__two_images.lif"
bioimage = BioImage(image_url)
print(bioimage)
print(type(bioimage))

# %%
# Inspect number of images in object
print(bioimage.scenes)

# %%
# Inspect both images in the object
for image in bioimage.scenes:
    print(f'Image name: {image}')
    # Select image:
    bioimage.set_scene(image)
    # Inspect dimension and shape of image
    print(f'Image dimension: {bioimage.dims}')
    print(f'Dimension order is: {bioimage.dims.order}')
    print(f'Image shape: {bioimage.shape}')
    # Extract image data (5D)
    image_data = bioimage.data
    print(f'Image type: {type(image_data)}')
    print(f'Image array shape: {image_data.shape}')
    # Extract specific image part
    image_data = bioimage.get_image_data('YX')
    print(f'Image type: {type(image_data)}')
    print(f'Image array shape: {image_data.shape}')
    # Read pixel size
    print(f'Pixel size: {bioimage.physical_pixel_sizes}')
    # Read metadata
    print(f'Metadata type: {type(bioimage.metadata)}')
    print(f'Metadata: {bioimage.metadata}')
    print('\n')

#%%
# Handle metadata
import xml.etree.ElementTree as ET
from xml.dom import minidom
# print a pretty version of the xml metadata
md = bioimage.metadata
xml_string = ET.tostring(md, encoding='unicode')
pretty_xml = minidom.parseString(xml_string).toprettyxml(indent="  ")
print(pretty_xml)

#%%
# Grab specific information
size_x = md.find(".//DimensionDescription").get('NumberOfElements')
pixel_size_x = float(md.find(".//DimensionDescription").get('Length'))
pixel_size_x_unit = md.find(".//DimensionDescription").get('Unit')
print(f'Image size in X: {size_x}')
print(f'Pixel size x: {pixel_size_x} in {pixel_size_x_unit}')
channels = md.findall(".//MultiBand")
excitation_wavelength = channels[1].get("TargetWaveLengthBegin")
emission_wavelength = channels[1].get("TargetWaveLengthEnd")
dye_name = channels[1].get("DyeName")
print(f'Excitation wavelength: {excitation_wavelength}')
print(f'Emission wavelength: {emission_wavelength}')
print(f'Dye name: {dye_name}')

Open volume EM TIFF series

Open a series of 2D TIFF files representing an EM volume
Observe the data is distributed onto several files on disk
Observe that crucial metadata, namely the z slice position, is part of the file name
Observe that the pixel calibration metadata is wrong
- Try to find the metadata at the EMPIAR website
Discuss that for a 2D file series, it is not obvious where to store the z spacing, because the individual files are only 2D and thus may not have a metadata tag for the z-dimension
Discuss that using pattern files might be useful for such data that is distributed across multiple files

Data

EM-TIFF-Series.ZIP
- The data is from https://www.ebi.ac.uk/empiar/EMPIAR-10982/
- If the above link does not work, please download from here

Show activity for:

ImageJ GUI

Open the file mentioned in the activity using:

[Plugins > Bio-Formats > Bio-Format Importer]

Select one of the EM TIFF slice files

Group files with similar names

This will load all the slices (see below)

Display metadata

Display OME-XML Metadata

Press [OK]

A dialog will appear that lets you configure how to load the multiple files “with similar names”

Study the dialog, but you should not need to change anything

Press [OK] to open the TIFF series

Use [Image > Properties] to observe that the pixel size metadata is missing/wrong

Visit https://www.ebi.ac.uk/empiar/EMPIAR-10982/ to find and then enter the pixel calibration data

python BioIO

# %% 
# Open a TIFF series
# minimal conda env for this module
# conda create -n ImageFileFormats python=3.10
# activate ImageFileFormat
# pip install bioio bioio-tifffile bioio-lif bioio-czi bioio-ome-tiff bioio-ome-zarr notebook
# Note: for only dealing with .tif just do pip install bioio bioio-tifffile


# %%
# create list of image file names and order them
from pathlib import Path
path_to_files = Path('/Users/fschneider/Training/training-resources/image_data/xyz_8bit__em_volume_tiff_series')
tiff_files = [file for file in path_to_files.glob("*.tif")]
# order files
tiff_files.sort()
print(tiff_files)

# %%
# Open each image with BioIO
from bioio import BioImage
bioimages = [BioImage(file) for file in tiff_files]

# Print pixel sizes
for bioimage in bioimages:
    print(bioimage.physical_pixel_sizes)

# %%
# Concatenate in 3D volume
em_volume = [bioimage.data.squeeze() for bioimage in bioimages]
# make numpy array
import numpy as np
em_volume = np.stack(em_volume,axis=0)
print(em_volume.shape)

# %%
# Lazy load
import dask.array as da
em_volume = [bioimage.dask_data[0,0,:,:,:] for bioimage in bioimages]
em_volume = da.stack(em_volume,axis=0)
print(em_volume.shape)

Open Olympus VSI image data

Open an Olypmus VSI dataset and inspect its pixel and metatdata content
Observe that one 3-D image is stored as multiple files and folders

Data

VSI image data

Show activity for:

ImageJ GUI

Drag and drop the .VSI file on Fiji

What happens?

For us, it seemingly opened the image data wrongly, showing some weird RGB data

Now open the VSI file using [Plugins > Bio-Formats > Bio-Formats Importer]

Display metadata

Display OME-XML Metadata

Press [OK]

In the upcoming dialog select the first image “series”, which represents the actual image data

Press [OK]

The image data and metadata will open and can be inspected

Copy the VSI file to a different location and try again opening it. This should NOT work anymore, because the link to actual data, which is in the ETS file, is now broken

python BioIO

# TODO: Change the below code to open the VSI dataset

# %% 
# Open a VSI image file
# minimal conda env for this module
# conda create -n ImageFileFormats python=3.10
# activate ImageFileFormat
# pip install bioio bioio-tifffile bioio-lif bioio-czi bioio-ome-tiff bioio-ome-zarr notebook
# Note: for only dealing with .lif just do pip install bioio bioio-lif


# %%
# Load .lif file
# - Observe that BioImage chooses the correct reader plugin
from bioio import BioImage
image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_xyc__two_images.lif"
bioimage = BioImage(image_url)
print(bioimage)
print(type(bioimage))

# %%
# Inspect number of images in object
print(bioimage.scenes)

# %%
# Inspect both images in the object
for image in bioimage.scenes:
    print(f'Image name: {image}')
    # Select image:
    bioimage.set_scene(image)
    # Inspect dimension and shape of image
    print(f'Image dimension: {bioimage.dims}')
    print(f'Dimension order is: {bioimage.dims.order}')
    print(f'Image shape: {bioimage.shape}')
    # Extract image data (5D)
    image_data = bioimage.data
    print(f'Image type: {type(image_data)}')
    print(f'Image array shape: {image_data.shape}')
    # Extract specific image part
    image_data = bioimage.get_image_data('YX')
    print(f'Image type: {type(image_data)}')
    print(f'Image array shape: {image_data.shape}')
    # Read pixel size
    print(f'Pixel size: {bioimage.physical_pixel_sizes}')
    # Read metadata
    print(f'Metadata type: {type(bioimage.metadata)}')
    print(f'Metadata: {bioimage.metadata}')
    print('\n')

Explore various image file formats

Open different image file formats and inspect their pixel data and also their metadata
Appreciate that image file formats
- include more or less metadata
- may contain multiple images
- may consist of multiple files, e.g. splitting metadata and pixel data across two files

Example image data

Show activity for:

ImageJ GUI

Open the files mentioned in the activity:

[Plugins > Bio-Formats > Bio-Format Importer].

Display metadata

Display OME-XML Metadata

Should be the same information as above but in XML (sometimes it is more correct than the above)

For ICS/IDS and XML/HDF5:

The ICS and XML file are the entry points that should be opened (the respective other file will be read automatically).

Also inspect the ICS and XML files in a simple text editor.

Saving 8 bit single channel image as TIFF:

Open xy_8bit__nuclei_PLK1_control.tif

[Image > Adjust > Brightness/Contrast] such that cells appear saturated

[File > Save As > TIFF…]

Open with Fiji

LUT metadata has changed, but pixel values and calibration metadata are preserved

Open with a web browser

It may not open at all

Saving 8 bit single channel image as JPEG:

Open xy_8bit__nuclei_PLK1_control.tif

[Image > Adjust > Brightness/Contrast] such that cells appear saturated

[File > Save As > JPEG…]

Open with Fiji

Pixel values have changed

Calibration metadata is gone

Open with a web browser

It should look the same as when you saved it

Saving 16 bit two channel movie as JPEG: xyzct_16bit__mitosis.tif

Select a timepoint in the middle of the movie

[File > Save As > JPEG…]

Open JPEG with Fiji

Image dimensions, data type, pixel values, and metadata have changed

Saving 8 bit single channel movie as GIF: xyt_8bit__mitocheck_incenp.tif

[Image > Adjust > Brightness/Contrast] such that cells appear saturated

[File > Save As > GIF…]

Open with Fiji

Pixel values have changed

Open with a web browser

Movie plays and looks as when you saved it

python BioIO

# %% 
# Load different image files and access various levels of metadata
# minimal conda env for this module
# conda create -n ImageFileFormats python=3.10
# activate ImageFileFormat
# pip install bioio bioio-tifffile bioio-lif bioio-czi bioio-ome-tiff bioio-ome-zarr notebook

# IMPORTANT: bioio by default expects z,y,x to be in microns and T to be in frames per second

# %%
# Load .tif file with minimal metadata
# - Observe that BioImage chooses the correct reader plugin
# - Observe that the return object is not the image matrix
from bioio import BioImage
image_url = 'https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_PLK1_control.tif'
bioimage = BioImage(image_url)
print(bioimage)
print(type(bioimage))

# %%
# Print some onject attributes
# - Observe that the object is 5 dimensional with most dimensions being empty
# - Observe that the dimension order is always time, channel, z, y, x, (TCZYX)
print(bioimage.dims)
print(bioimage.shape)
print(f'Dimension order is: {bioimage.dims.order}')
print(type(bioimage.dims.order))
print(f'Size of X dimension is: {bioimage.dims.X}')

# %%
# Extract image data
# - Observe that the returned numpy.array is still 5 dimensional
image_data = bioimage.data
print(type(image_data))
print(image_data)
print(image_data.shape)

# %%
# Extract specific part of image data
# - Observe that numpy.array is reduced to populated dimensions only
yx_image_data = bioimage.get_image_data('YX')
print(type(yx_image_data))
print(yx_image_data)
print(yx_image_data.shape)

# %%
# Access pixel size
import numpy as np
print(bioimage.physical_pixel_sizes)
print(f'An pixel has a length of {np.round(bioimage.physical_pixel_sizes.X,2)} microns in X dimension.')

# %%
# Access general metadata
print(type(bioimage.metadata))
print(bioimage.metadata)

# %%
# Load .tif file with extensive metadata
image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_16bit__collagen.md.tif"
bioimage = BioImage(image_url)
print(bioimage)
print(type(bioimage))

# - Observe that the image is larger than the previous
print(bioimage.dims)

# %%
# Access image and reduce to only populated dimensions
yx_image_data = bioimage.data.squeeze()
print(type(yx_image_data))
print(yx_image_data)
print(yx_image_data.shape)

# %%
# Access pixel size
print(bioimage.physical_pixel_sizes)
print(f'An pixel has a length of {np.round(bioimage.physical_pixel_sizes.Y,2)} microns in Y dimension.')

# Access general metadata
# - Observe that metadata are more extensive than in the previous image
print(type(bioimage.metadata))
print(bioimage.metadata)

# %%
# Load .lif file
# - Observe that BioImage chooses the correct reader plugin
# - Observe that the return object has 4 different channels
# - Observe that the general metadata are an abstract element
image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_xyc__two_images.lif"
bioimage = BioImage(image_url)
print(bioimage)
print(type(bioimage))
print(bioimage.dims)
print(bioimage.metadata)
print(type(bioimage.metadata))

# %%
# Access channel information
print(bioimage.channel_names)

# %%
# Access image data for all channels
img_4channel = bioimage.data.squeeze()

# Alternative
img_4channel = bioimage.get_image_data('CYX')

# - Observe that numpy.array shape is 3 dimensional representing channel,y,x
print(img_4channel.shape)

# Access only one channel
img_1channel = bioimage.get_image_data('YX',C=0)

# Alternative
img_1channel = img_4channel[0]

# - Observe that numpy.array shape is 2 dimensional representing y,x
print(img_1channel.shape)

# %%
# Access different images in one image file (scenes)
# - Observe that one image file can contain several scenes
# - Observe that they can be different in various aspects
print(bioimage.scenes)
print(f'Current scene: {bioimage.current_scene}')

# - Observe that the image in the current scene as 4 channel and Y/X dimensions have the size of 1024
print(bioimage.dims)
print(bioimage.physical_pixel_sizes)

# Switch to second scene
# - Observe that the image in the other scene as only one channel and Y/X dimensions are half as large as the first scene
# - Observe that the pixel sizes are doubled
bioimage.set_scene(1)
print(bioimage.dims)
print(bioimage.physical_pixel_sizes)

# %%
# Load .czi file
# file needs first to be downloaded from https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz__multiple_images.czi
# save file in the same directory as this notebook
# - Observe that BioImage chooses the correct reader plugin
# - Observe that the return object has a z dimension
bioimage = BioImage('/Users/fschneider/skimage-napari-tutorial/ExampleImages/xyz__multiple_images.czi')
print(bioimage)
print(type(bioimage))

# %%
# little excersise in between
# Access image dimensions
print(bioimage.dims)

# Access general metadata
# - Observe that metadata are abstract
print(bioimage.metadata)
print(type(bioimage.metadata))

# Access pixel size
print(bioimage.physical_pixel_sizes)

# Access image data for all channels
img_3d = bioimage.data.squeeze()

# Alternative
img_3d = bioimage.get_image_data('ZYX')

# - Observe that numpy.array shape is 3 dimensional representing z,y,x
print(img_3d.shape)

# Access only one channel
img_2d = bioimage.get_image_data('YX',Z=0)

# Alternative
img_2d = img_3d[0]

# - Observe that numpy.array shape is 2 dimensional representing y,x
print(img_2d.shape)

# %%
# little excercise:
# paticipants should try to open one of their files with python

Resave images in various file formats

Resaving images in different file formats very often leads to a loss of metadata or distortion of the pixel values. It is critical to be aware of this!

Checks to be done after each resaving

Open the resaved image in all relevant applications and check whether the pixels values and/or metadata are different from the original image
- This is critical for the scientific integrity of the resaving
Open the image in a web-browser and observe how the image is rendered
- This can be useful to share previews with collaborators

Resave 8 bit single channel image as TIFF

Open xy_8bit__nuclei_PLK1_control.tif
Change the contrast limits
Save as TIFF
Do the above checks

Resave 8 bit single channel image as JPEG

Open xy_8bit__nuclei_PLK1_control.tif
Change contrast such that the cells appear saturated
Save as JPEG
Do the above checks

Resave 16 bit two channel movie as JPEG

Open xyzct_16bit__mitosis.tif
Select a timepoint in the middle of the movie
Save as JPEG
Do the above checks

Resave 8 bit single channel movie as GIF

Open xyt_8bit__mitocheck_incenp.tif
Change contrast such that cells appear saturated
Save as GIF
Do the above checks

Show activity for:

ImageJ GUI

Saving 8 bit single channel image as TIFF:

Open xy_8bit__nuclei_PLK1_control.tif

[Image > Adjust > Brightness/Contrast] such that cells appear saturated

[File > Save As > TIFF…]

Open with Fiji

LUT metadata has changed, but pixel values and calibration metadata are preserved

Open with a web browser

It may not open at all

Saving 8 bit single channel image as JPEG:

Open xy_8bit__nuclei_PLK1_control.tif

[Image > Adjust > Brightness/Contrast] such that cells appear saturated

[File > Save As > JPEG…]

Open with Fiji

Pixel values have changed

Calibration metadata is gone

Open with a web browser

It should look the same as when you saved it

Saving 16 bit two channel movie as JPEG: xyzct_16bit__mitosis.tif

Select a timepoint in the middle of the movie

[File > Save As > JPEG…]

Open JPEG with Fiji

Image dimensions, data type, pixel values, and metadata have changed

Saving 8 bit single channel movie as GIF: xyt_8bit__mitocheck_incenp.tif

[Image > Adjust > Brightness/Contrast] such that cells appear saturated

[File > Save As > GIF…]

Open with Fiji

Pixel values have changed

Open with a web browser

Movie plays and looks as when you saved it

Assessment

True or false

One could use Excel’s XLSX file format for saving image data.

Solution

One could use Excel’s XLSX file format for saving image data. True, the matrix of each sheet could represent one image plane and one could use the first sheet to store metadata and the mapping of each sheet (image plane) to the zct coordinates, e.g. sheet 12 c 2 z 3 t 1.

Discuss

What are the pros and cons of converting an image into another format?
What are the pros and cons of splitting metadata and image pixel data into separate files?
Do you know any good file formats for image metadata?

Solution

(A) Sometimes it is necessary to convert to another format to be able to open the image in a specific software. (B) Converting an image to another format typically loose information, e.g. because the file format that you are saving to cannot represent all the metadata of the original image file. Thus, it is in general recommened to keep to original image file. (C) Converting to a file format with good compression may save you considerable disk space.

(A) Metadata typically is much smaller than the pixel data. Thus, it can be a good idea to keep metadata in a separate file that can be readily inspected (inspecting the potentially TB sized pixel data files can be tricky). (B) The best file formats for metadata and pixel data can be very different due to the nature of the data, thus splitting can make sense. (C) Having separate files always bares the risk that you loose one of them, e.g. you may forget to copy both to a new folder.

TXT, XML, and JSON are good formats for image metadata, because they are human readable standard formats that can be openend with any text editor.

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