Loading Microscopy Volumes

Image volumes created by advanced micrography, such as confocal imaging, are best loaded into MATLAB using the Bio-formats MATLAB toolbox, bfmatlab. This toolbox can handle the proprietary file formats generated by all of the major microscope manufacturers. Saving image stacks in the proprietary file format of the microscope best ensures that the full resolution and bit-depth of the image data is preserved, along with all captured metadata.

In this example, we will load a 4D image stack of a taste bud.

This image stack has 4 Dimensions. Its size is 512x512x3x12, which means that each individual slice in the stack is 512x512 and has three channels (like an RGB image). And there are 12 of these slices.

mmReadImgND

The course function mmReadImgND is a wrapper for the tools included in the bfmatlab toolbox. The following syntax loads a color image stack created by a confocal microscope.

[RGB4,meta] = mmReadImgND("2641-tastebud-RGB.tif");

Reviewing the workspace shows the size and class of RGB4…

result

  Name        Size                    Bytes  Class     Attributes

  RGB4      512x512x3x12            9437184  uint8               
  meta        1x1                      3775  struct              

…RGB4 is an 8-bit 4D array. It has 3 channels and 12 image slices. Each image slice in the volume has 512 rows (Height) and 512 columns (Width).

Reviewing the meta structure reveals the same information, along with size of the voxels (PixelSpacing)…

meta = struct with fields:
            filename: "2641-tastebud-RGB.tif"
    StudyDescription: '2641-tastebud-RGB.tif'
      SeriesSelected: 0
               Width: 512
              Height: 512
            BitDepth: 'uint8'
        PixelSpacing: [0.30333 0.30333 0.75531]
        ChannelCount: 3
          PlaneCount: 12
           TimeCount: 1
                 XML: "<?xml version="1.0" encoding="utf-8"?>↵<OME xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openmicroscopy.org/Schemas/OME/2016-06 http://www.openmicroscopy.org/Schemas/OME/2016-06/ome.xsd">↵<Image ID="Image:0" Name="2641-tastebud-RGB.tif">↵<Description/>↵<Pixels BigEndian="true" DimensionOrder="XYCZT" ID="Pixels:0" Interleaved="false" PhysicalSizeX="0.303326643969745" PhysicalSizeXUnit="µm" PhysicalSizeY="0.303326643969745" PhysicalSizeYUnit="µm" PhysicalSizeZ="0.755310778914241" PhysicalSizeZUnit="µm" SignificantBits="8" SizeC="3" SizeT="1" SizeX="512" SizeY="512" SizeZ="12" Type="uint8">↵<Channel ID="Channel:0:0" SamplesPerPixel="1">↵<LightPath/>↵</Channel>↵<Channel ID="Channel:0:1" SamplesPerPixel="1">↵<LightPath/>↵</Channel>↵<Channel ID="Channel:0:2" SamplesPerPixel="1">↵<LightPath/>↵</Channel>↵<MetadataOnly/>↵</Pixels>↵</Image>↵</OME>"

in this image matrix.

Indexing Image Stacks

Volume processing often requires indexing the stack to pull out smaller stacks or individual slices.

Indexing image stacks is fairly straightforward — you just need to keep track of the Dimensions. For example, we can index out the Red channel using the following syntax:

RED = RGB4(:,:,1,:); % all rows, all columns, channel 1, all z-slices

…This returns a 4D array with just 1 channel

Size of RED

size(RED)

ans =

   512   512     1    12

We can drop the singleton dimension (dimension three) using the function squeeze.

Squeeze 4D array into a 3D array

RED = squeeze(RED);

size(RED)

ans =

   512   512   12

…Now RED is a 3D array.

We can index a single slice out of RED using the following syntax:

slice = RED(:,:,6)

Size of slice

size(slice)

ans =

   512   512

…slice is a 512x512 image.

If we wanted all three channels in a slice, we simply index the original, 4D volume, RGB4.

rgb_slice = RGB4(:,:,:,6); % all rows, all columns, all channels, slice 6

Size of slice

size(rgb_slice)

ans =

   512   512   3

…rgb_slice is an image with 3 channels (RGB image).

So, we can index out a color channel by accessing the third dimension and index out a z-plane by accessing the 4th dimension.