Denoising images using non-local means

[Garyfallidis]

In this PR I am adding a new general way to apply denoising in 3D and 4D images using NLMEANS. I have rewrote and optimized the algorithm. For the first time we use OpenMP in dipy through cython. OpenMP increases considerable execution time when you want to use multi-threading with shared memory.

A few things that are missing and they should come soon in another PR.

1. estimate SNR for each of the 3D volumes of a DWI dataset
2. estimate SNR for T1 and add an example of T1 denoising in the existing example.
3. explain that we first estimate the noise and then apply the mask
4. add any other references.
5. extend the method to its adaptive version which is what we used for the ISBI competition
6. make sure that the results correspond with Pierrick's matlab code.

@samuelstjean will work on these steps as I need to focus on a different project and denoising is his main interest.

GGT!

[stefanv]

Unless you have awesome models, of course :)

[samuelstjean]

inside

[samuelstjean]

Seems good, it change the code and example afterward I guess.

[mdesco]

Another question/comment. I just denoised a 500Mb full brain dataset and my RAM consumption went up to 3 Giga. So, a factor of 6 increase.

I see that I inputed an float32 file and the output was a float64. So, this might be contributing to the issue.

Do you think that if the input is int16 or float32, the output should as well? My old binary used to do that.

Still fast and good results!

[samuelstjean]

Should be the responsability of the user to save it himself as another dtype I'd say. Dunno for the ram usage though.

[Garyfallidis]

@mdesco good catch. Please give it a try with the a new fix. It should be okay now. Please let me know otherwise.

[mdesco]

I still have a large RAM consumption. 251Mb input and my RAM goes up to 3Gig during the processing.

[Garyfallidis]

What type of dataset is the one that you use? Is it a 4D image of dMRI data?

[Garyfallidis]

Hi Max, I am not able to replicate your problem. I monitored the memory usage with a 4D dataset of 400MB and the memory needed at the end and during execution time was an extra 400MB as expected. So, the total amount of memory was ~800MB. I would suggest maybe to recompile your dipy installation after having fetch my latest changes for this brunch. If that doesn't work then let's have a look together at IMEKA.

[mdesco]

All good on my side. It works fine and nlmeans supports 4D. Thanks.

[Garyfallidis]

Okay, guys. This is ready on my side. Can someone who is not from the SCIL give it a last review and merge this baby?

[arokem]

Typo : "denoise"

[arokem]

I will take a look.

On Mon, Feb 24, 2014 at 6:19 AM, Eleftherios Garyfallidis <
notifications@github.com> wrote:

Okay, guys. This is ready on my side. Can someone who is not from the SCIL
give it a last review and merge this baby?

—
Reply to this email directly or view it on GitHubhttps://github.com//pull/320#issuecomment-35889891
.

[arokem]

The docstring suggests that either 4D or 3D arrays are acceptable inputs. Which is it?

[Garyfallidis]

Good catch. Thx!

[arokem]

I am getting this error when running the example. Are you sure the input to the bbox_inches kwarg makes sense?

AssertionError Traceback (most recent call last)
/Users/arokem/Desktop/denoise_nlmeans.py in ()
49 plt.imshow(den[:, :, axial_middle].T, cmap='gray', origin='lower')
50 plt.show()
---> 51 plt.savefig('denoised_S0.png', bbox_inches='tight')
52
53 """

/Users/arokem/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/pyplot.pyc in savefig(_args, *_kwargs)
559 def savefig(_args, *_kwargs):
560 fig = gcf()
--> 561 return fig.savefig(_args, *_kwargs)
562
563

/Users/arokem/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/figure.pyc in savefig(self, _args, *_kwargs)
1419 self.set_frameon(frameon)
1420
-> 1421 self.canvas.print_figure(_args, *_kwargs)
1422
1423 if frameon:

/Users/arokem/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/backend_bases.pyc in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, *_kwargs)
2167 *_kwargs)
2168 renderer = self.figure._cachedRenderer
-> 2169 bbox_inches = self.figure.get_tightbbox(renderer)
2170
2171 bbox_artists = kwargs.pop("bbox_extra_artists", None)

/Users/arokem/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/figure.pyc in get_tightbbox(self, renderer)
1562 bb.append(ax.get_tightbbox(renderer))
1563
-> 1564 _bbox = Bbox.union([b for b in bb if b.width != 0 or b.height != 0])
1565
1566 bbox_inches = TransformedBbox(_bbox,

/Users/arokem/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/transforms.pyc in union(bboxes)
712 Return a :class:Bbox that contains all of the given bboxes.
713 """
--> 714 assert(len(bboxes))
715
716 if len(bboxes) == 1:

[Garyfallidis]

Hi @arokem I added all your suggestions except from the bbox_inches='tight'. I think this problem is on your side. This option is recommended in many places. Let me know if the problem persists. Could it be that you have some problems with your matplotlib installation or version? My version is 1.2.1.

Also if you know a better way to remove the padding from the figure let me know.