Posted by
Herbie-3 on
Mar 20, 2014; 10:02am
URL: http://imagej.273.s1.nabble.com/Rephrased-FFT-Question-tp5007000p5007010.html
Rebecca,
if you manage to extract 1D-"images" from your time series of images,
i.e. in fact pixel sequences as 1D-images, then you may use the
FFTJ-PlugIn to perform 1D-FFTs of each of these series.
<
http://rsb.info.nih.gov/ij/plugins/fftj.html>
You may use a macro to extract the n^2 1D-images (assuming that your
images are of size n x n). To make use of the FFT speed advantage, your
stack must consist of m images (time slices), where m is a power of two.
The problem with FFTJ is, that it isn't perfectly macro-recordable. It
shows a dialog for choosing the result format. In other words, to fully
automatize processing by an IJ-macro, you need to modify the source code
of FFTJ.
HTH
Herbie
::::::::::::::::::::::::::::::::::::::::
On 20.03.14 02:04, Rebecca Keller wrote:
> Thanks very much for this reference, but actually it is a bit
> cumbersome to come in and out of imagej, since this "plugin" requires
> ms excel for the fft'ing.
>
> Seems like it should be possible to get the current fft algorithm to
> do a 1D FFT on each pixel over time, then re-output the timelapse
> stack as a 2D image in frequency domain. Then it would be neat, for
> example, to make a 2D image depth-coded by frequency. I, however,
> just need to get the pixels which all have the same frequencies,
> which should be easy enough to do using the 1D FFT pixelwise which I
> suggested.
>
> JPK
>
> On Wed, Mar 19, 2014 at 5:18 PM, Eric Denarier <
>
[hidden email]> wrote:
>
>> Hi Rebecca, You may have a look to Claire Smith's CiliaFA macro. It
>> reveals frequencies of intensity variation in different zone of a
>> brightfield image. We are using it for high frequency Cilia
>> beating. here is a link to the paper and macro :
>>
http://www.ciliajournal.com/content/1/1/14>>
>>
>> Rebecca Keller <
[hidden email]> a écrit :
>>
>> Dear List,
>>>
>>> Let me rephrase: what I am trying to do is locate oscillations of
>>> a certain frequency onto a 2D visual field, i.e., convert the
>>> third dimension (time) of a time-lapse image series into
>>> frequency space. Then I could scan through frequency space (the
>>> new third dimension) to find groups of pixels with similar
>>> oscillation frequencies.
>>>
>>> So I guess what would need to be done is a 1D FFT for each pixel.
>>> Is there any way easily to do this with the current imagej
>>> tools?
>>>
>>> JPK
>>>
>>> -- ImageJ mailing list:
http://imagej.nih.gov/ij/list.html>>>
>>
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http://imagej.nih.gov/ij/list.html>>
>
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