[ITK] [ITK-users] Mattes Mutual Information can produce a false positive?

[David Welch]

You're most likely finding a local minimum - you can't discount the
implementation just because the minimization value is high.

For example, two images of different patients would produce a "high"
minimization value even though the registration might be very good.  Each
registration problem is unique, so you haven't found a "smoking gun" yet.

Try plotting the metric value over the range of accepted values for your
transform and I'll wager you'll see that the minimization is converging on
a local minimum somewhere.  That will tell you what parameter of your
optimization you need to tweak.

On Sunday, January 3, 2016, Gabriel A. Giménez <gabrielgimenez85 at gmail.com>
wrote:

> Hi again...
> Thanks for your answer David, I was doing more tests to be sure of the
> problem. I do not know the implementation of metrics and is beyond of the
> scope of my work, but definitive think it's a bug of the implementation.
>
> This is the transformation that generates the error, and appears at random:
>
> Metric value:  -0.571747,
> Transformation:   [0.45776567727601714, 0.041654583723424364,
> -0.04058569867934432, -0.9751276082246223, -35.48376926584542,
> -28.108117798651822]
>
> My optimizer generates random solutions, perhaps it could be generating an
> "rare"  solutions ... but the metric should not calculate a high value for
> this types of solutions, no ?
>
> Attached results (difference before and after) to apply the transformation.
>
> Thanks...
>
>
> 2015-12-16 14:55 GMT-03:00 David Welch <david.m.welch at gmail.com
> <javascript:_e(%7B%7D,'cvml','david.m.welch at gmail.com');>>:
>
>> Hi Gabriel,
>>
>> I’m a little rusty, so maybe someone else can correct my example.
>>
>> 1) Consider a circle with one half “dark: and one half “bright" with a
>> gray background.
>> 2) Then consider the inverted image (max() - image).
>> 3) Rotate the inverted image randomly about the center.
>> 4) Now register the two using Mattes’.
>>
>> Do you see the problem? Statistically, both pairings of bright->bright
>> and bright->dark have the same entropy to Mattes’, so neither would be
>> preferred and the algorithm would settle on which ever solution it reaches
>> first and would give a false positive 50% of the time.
>>
>> So Mattes’ CAN produce false positives depending on the underlying
>> statistics for the image pairs.
>>
>> Cheers,
>> Dave
>>
>>
>>
>>
>> On 12/15/15, 2:25 PM, "Gabriel A. Giménez" <gabrielgimenez85 at gmail.com
>> <javascript:_e(%7B%7D,'cvml','gabrielgimenez85 at gmail.com');>> wrote:
>>
>> >Hi all, I hope you are well...
>> >
>> >My question is about a implementation of Mattes Mutual Information...is
>> >possible to produce a false positive ? at times it gives me a high metric
>> >value ... but the result of the recording is poor. We use it as a metric
>> for
>> >two implementations of optimizers (PSO and Scatter search).
>> >
>> >Attached results of two executions, using scatter search:
>> >
>> >Moving image: mr_pd
>> >Fixed image: ct
>> >Images source: http://www.insight-journal.org/rire/download_data.php
>> >(patient_001)
>> >
>> >Thank you very much and I hope you can help me, regards.
>> >
>> >
>> >ct_mr_pd_incorrect_result.zip
>> ><
>> http://itk-insight-users.2283740.n2.nabble.com/file/n7588261/ct_mr_pd_incorrect_result.zip
>> >
>> >ct_mr_pd_accurate_result.zip
>> ><
>> http://itk-insight-users.2283740.n2.nabble.com/file/n7588261/ct_mr_pd_accurate_result.zip
>> >
>> >
>> >
>> >
>> >--
>> >View this message in context:
>> http://itk-insight-users.2283740.n2.nabble.com/Mattes-Mutual-Information-can-produce-a-false-positive-tp7588261.html
>> >Sent from the ITK Insight Users mailing list archive at Nabble.com.
>> >
>>
>>
>
>
> --
> *Gabriel Alberto Giménez.*
>


-- 
Sent from a mobile device
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