[MINC-users] Classify

[Jon Erik Ween]

Thanks Jason

So, I tag 100 white matter, 100 grey [cortical and deep] voxels,  
fairly evenly distributed throughout the brain) using Display, with  
different label values for each tissue type (does the value matter?)  
and save as a .tag file?

You do this on the intensity normalized volume and not the native  
image, right?

Is this process sensitive to changes in scanner software? We upgrade  
the magnets from time to time.

Where do I find an appropriate probability maps? I'm supposing the MNI  
averages are not the ones. Do the autoreg models work for this?

Thanks

Jon

Soli Deo Gloria

Jon Erik Ween, MD, MS
Scientist, Kunin-Lunenfeld Applied Research Unit
Director, Stroke Clinic, Brain Health Clinic, Baycrest Centre
Assistant Professor, Dept. of Medicine, Div. of Neurology
     University of Toronto Faculty of Medicine

Kimel Family Building, 6th Floor, Room 644
Baycrest Centre
3560 Bathurst Street
Toronto, Ontario M6A 2E1
Canada

Phone: 416-785-2500 x3648
Fax: 416-785-2484
Email: jween at klaru-baycrest.on.ca


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On 25-Mar-09, at 12:51 PM, Jason Lerch wrote:

> Hi Jon,
>
> classify is based on using a training set to initialize the tissue
> classification - either based on probability maps or on manually
> selected tags. In other words, you could use Display to pick 100
> points or so that you think are grey matter, 100 that are white
> matter, etc., then use that to initialize the classification of every
> voxel. Once you've trained the classifier once you can also save the
> training data and apply it to new volumes without repicking individual
> points (though this is quite scanner specific - i.e. it usually works
> for one sequence from one site, not across sequences or sites - for
> that probability maps are better).
>
> classify_clean uses classify but prunes those initial points first to
> remove any outliers - this can be a benefit when you use spatial
> priors based on probability maps.
>
> Hope this helps,
>
> Jason
>
>
>
>
> On 25-Mar-09, at 12:18 PM, Jon Erik Ween wrote:
>
>> Dear minclist.
>>
>> Sorry for the incremental posts, but then, how is one to learn??
>>
>> Anyway, thanks to help from the list, I'm now in the possession of
>> skull-stripped, talairach registered, lesion masked 3DT1 images of a
>> group of patients and would like to tissue classify these volumes  
>> (and
>> hopefully label major lobes etc) so I can calculate tissue and lesion
>> volumes (globally and in particular lobes, etc). Running
>> "classify" (or classify_clean, I can't figure out the difference
>> between these two, exactly) complains that I don't have a tag file  
>> (or
>> in the case of classify_clean, that the tags are not in the  
>> volume). I
>> was thinking that "classify" used the specified model to figure out
>> which voxels were which tissue, or do I need to manually tag voxels  
>> in
>> each of the target volumes?
>>
>> I, again, appreciate any guidance!
>>
>> Cheers
>>
>> Jon
>>
>> Soli Deo Gloria
>>
>> Jon Erik Ween, MD, MS
>> Scientist, Kunin-Lunenfeld Applied Research Unit
>> Director, Stroke Clinic, Brain Health Clinic, Baycrest Centre
>> Assistant Professor, Dept. of Medicine, Div. of Neurology
>>    University of Toronto Faculty of Medicine
>>
>> Kimel Family Building, 6th Floor, Room 644
>> Baycrest Centre
>> 3560 Bathurst Street
>> Toronto, Ontario M6A 2E1
>> Canada
>>
>> Phone: 416-785-2500 x3648
>> Fax: 416-785-2484
>> Email: jween at klaru-baycrest.on.ca
>>
>>
>> Confidential: This communication and any attachment(s) may contain
>> confidential or privileged information and is intended solely for the
>> address(es) or the entity representing the recipient(s). If you have
>> received this information in error, you are hereby advised to destroy
>> the document and any attachment(s), make no copies of same and inform
>> the sender immediately of the error. Any unauthorized use or
>> disclosure of this information is strictly prohibited.
>> _______________________________________________
>> MINC-users at bic.mni.mcgill.ca
>> http://www2.bic.mni.mcgill.ca/mailman/listinfo/minc-users
>
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