[MINC-users] classify: how to use

[Jason Lerch]

Hi all, 

for those interested, we created a sort of replacement for classify:

https://github.com/Mouse-Imaging-Centre/classify-sklearn <https://github.com/Mouse-Imaging-Centre/classify-sklearn>

It’s based on python’s scikit-learn, and currently comes with a mix of classifiers (random forests, bayes, etc.). An example or two of how to use it is on the wiki part of that github page (though obviously more documentation is needed).

Biggest advantage over classify - doesn’t crash when outputting posterior probabilities with multispectral input - and doesn’t require the creation of a frankenbrain when training a classifier based on multiple subjects. Otherwise should be quite similar to the venerable old classify.

Doesn’t have a variant of classify_clean yet though.

Jason

> On May 3, 2016, at 7:21 PM, Andrew Janke <a.janke at gmail.com> wrote:
> 
> Hi Pedro,
> 
> This is my approach:
> 
>   classify_clean \
>         -tagdir <tags> \
>         -tag_transform <image-to-tags.xfm> \
>         input.mnc output.mnc
> 
> <tags> is a directory with the classify tags in it eg:
> 
> ls tags/
> ntags_1000_bg.tag  ntags_1000_prob_90_nobg.tag
> 
> 
> <image-to-tags.xfm> is a tranform from your native data space
> (input.mnc) to the space of the tags. If you are using the ones in the
> standard package this will be a transform from native space to the
> icbm152 model.
> 
> 
> a
> 
> 
> 
> On 3 May 2016 at 23:36, Pedro <ptcougopinto at gmail.com> wrote:
>> 
>> Hi, everyone —
>> 
>> What should be the minimum command line for classify? What are the inputs and output? I could´t figure out from -help. (Just found out that I´m not happy with registration based segmentation of my data).
>> 
>> Thanks!
>> Pedro
>> _______________________________________________
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