Load TIFF files into matlab fast, with lazy loading
This class allows you to access a TIFF file as a matlab tensor, while
only reading the data that you need from disk. A TIFFStack
object
appears like a four-dimensional tensor, with dimensions for rows,
columns, frames and channels (multiple samples per pixel). These objects
can be passed transparently into other functions that expect matlab
tensors. If you need to process only a portion, or only one channel of a
TIFF stack, then this class will save you allocating the enormous
amounts of memory required to load the entire file. TIFFStack
is also
much faster than using imread
to read each frame of the TIFF file
separately.
TIFFStack
attempts to use libTiff
, which is directly supported
in recent Matlab versions. This provides dramatic speed-ups, and is a good deal faster than using imread
or
the Matlab Tiff
class. If libTiff
is not available, then Matlab-only
code is used to read image data. permute
, ipermute
, transpose
and
ctranspose
are also transparently supported.
Clone TIFFStack
into a directory called
@TIFFStack. The ampersand symbol (@) is important, since it indicates
to Matlab that TIFFStack
is an object-oriented module. Add the parent
directory — not the @TIFFStack directory — to the Matlab path.
tsStack = TIFFStack(strFilename <, bInvert>)
A TIFFStack
object behaves like a read-only memory mapped TIFF file.
The entire image stack is treated as a Matlab tensor. Each frame of the
file must have the same dimensions. Reading the image data is optimised
to the extent possible; the header information is only read once.
This class attempts to use the Matlab libTiff interface, if available.
If not, it uses a modified version of tiffread
[1, 2] to read data.
Code is included (but disabled) to use the matlab imread
function, but
this function returns invalid data for some TIFF formats.
>> tsStack = TIFFStack('test.tiff'); % Construct a TIFF stack associated with a file
>> tsStack = TIFFStack('test.tiff', true); % Indicate that the image data should be inverted
tsStack =
TIFFStack handle
Properties:
bInvert: 1
strFilename: [1x9 char]
sImageInfo: [5x1 struct]
strDataClass: 'uint16'
>> tsStack(:, :, 3); % Retrieve the 3rd frame of the stack, all planes
>> tsStack(:, :, 1, 3); % Retrieve the 3rd plane of the 1st frame
>> size(tsStack) % Find the size of the stack (rows, cols, frames, planes per pixel)
ans =
128 128 5 1
>> tsStack(4); % Linear indexing is supported
>> tsStack.bInvert = true; % Turn on data inversion
Some TIFF generation software stores multiple samples per pixel as
interleaved frames in a TIFF file. Other complex stacks may include
multiple different images per frame of time (e.g. multiple cameras or
different imaged locations per frame). TIFFStack
allows these files to be
de-interleaved, such that each conceptual data dimension has its own
referencing dimension within Matlab.
This functionality uses the optional vnInterleavedFrameDims
argument.
This is a vector of dimensions that were interleaved into the single
frame dimension in the stack.
For example, a stack contains 2 channels of data per pixel, and 3 imaged locations per frame, all interleaved into the TIFF frame dimension. The stack contains 10 conceptual frames, and each frame contains 5x5 pixels.
The stack is therefore conceptually of dimensions [5 5 2 3 10 1], but appears on disk with dimensions [5 5 60 1]. (The final dimension corresponds to the samples-per-pixel dimension of the TIFF file).
>> tsStack = TIFFStack('file.tif', [], [2 3 10]);
>> size(tsStack)
ans =
5 5 2 3 10
Permutation and indexing now works seamlessly on this stack, with each conceptual dimension de-interleaved.
If desired, the final number of frames can be left off
vnInterleavedFrameDims
; for example
>> tsStack = TIFFStack('file.tif', [], [2 3]);
>> size(tsStack)
ans =
5 5 2 3 10
Note: You must be careful that you specify the dimensions in the appropriate order, exactly as interleaved in the stack. Also, if the stack contains multiple samples per pixel in native TIFF format, the samples-per-pixel dimension will always be pushed to the final dimension.
This work was published in Frontiers in Neuroinformatics: DR Muir and BM Kampa. 2015. FocusStack and StimServer: A new open source MATLAB toolchain for visual stimulation and analysis of two-photon calcium neuronal imaging data, Frontiers in Neuroinformatics 8 85. DOI: dx.doi.org/10.3389/fninf.2014.00085. Please cite our publication in lieu of thanks, if you use this code.
[1] Francois Nedelec, Thomas Surrey and A.C. Maggs. Physical Review Letters 86: 3192-3195; 2001. DOI: 10.1103/PhysRevLett.86.3192
This work optionally uses tiffread
from Francois Nedelec to access the data in
the TIFF file. Matlab includes the ability to read TIFF files in
imread
, including niceties such as only reading a region of interest
from each frame, but imread is incredibly slow and amazingly buggy (as
of July 2011). TIFFStack uses tiffread
in an optimised fashion, by
reading and caching the header information (the image file directories —
IFDs). Each frame can then be read directly without re-opening the file
and re-reading the IFDs.
TIFFStack by Dylan Muir is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at http://github.com/DylanMuir/TIFFStack.