[MINC-users] computing similarity across images

[Claude LEPAGE]

Hi,

Do you have only a few scans or hundreds of them? 

If only a few scans, linear registration with visual inspection will
do the trick. 

Claude



>
> Hi Trisanna,
>
> I assume you are talking about cases when the patient name/id may have been
> removed or is unreliable?
>
> I have done this in the past by performing a direct linear registration
> between two scans, and then calculating a similarity metric (e.g., xcorr)
> between them (using minccmp for example). Assuming there has not been too
> much change in the individual's brain between the scans, this can get you
> at least an indication that might be useful; but as Vladimir said, it's in
> general not trivial - and a measure like this will likely give you false
> positives.
>
> -- A
>
> On Mon, Mar 20, 2017 at 1:12 PM, Vladimir S. Fonov <vladimir.fonov at gmail.com
> > wrote:
>
> > Hello,
> >
> > in general this is not a trivial task.
> >
> > But from the practical point of view - did you try to use the contents of
> > "patient:full_name" field of the image header?
> >
> >
> >
> > On 2017-03-20 11:53 AM, Trisanna Sprung-Much wrote:
> >
> >> Hi there
> >>
> >> Is there a tool that can be used to confirm that 2 images are in fact
> >> images of the same patient's brain? For instance computing some sort of
> >> similarity index based on 2 T1 images or outputing a probability for them
> >> being images of the same brain?
> >>
> >> Thanks!
> >>
> >> Trisanna
> >>
> >> --
> >> Ph.D. Candidate
> >> McGill University
> >> Integrated Program in Neuroscience
> >> Psychology
> >> _______________________________________________
> >> MINC-users at bic.mni.mcgill.ca
> >> http://www.bic.mni.mcgill.ca/mailman/listinfo/minc-users
> >>
> >>
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> >
> >
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