[ITK-users] [ITK] Normalized Cross Correlation returns perfect alignment with images not even overlapping

[Dženan Zukić]

Hi Andrew,

it is good to use such additional constraints when possible. But you also
have to initialize the transform somehow, otherwise it might get
auto-initialized to all modifiable parameters being equal to zero. That is
usually a bad initial transform - hence Francois' suggestion.

Regards,
Dženan Zukić, PhD, Senior R&D Engineer, Kitware (Carrboro, N.C.)

On Tue, Apr 11, 2017 at 2:02 PM, Andrew Harris <aharr8 at uwo.ca> wrote:

> Hello Francois and Dženan,
>
> Because of the way the images were captured, there is a known common point
> of overlap, so we set the centre to that point in the expectation that the
> transform would rotate and translate about that point when the registration
> is run.  Have I misunderstood the design or is that what should be
> happening?
>
> --
>
> AH
>
>
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> On Tue, Apr 11, 2017 at 9:00 AM, Francois Budin <
> francois.budin at kitware.com> wrote:
>
>> Hello Andrew,
>>
>> Did you try to initialize the registration with [1] for example?
>> If the images do not overlap at all at the beginning of the registration,
>> the algorithm might only do what Dżenan said, match black pixels.
>> Initializing the transform should help.
>>
>> Hope this helps,
>> Francois
>>
>> [1] https://itk.org/Doxygen/html/classitk_1_1CenteredTransformIn
>> itializer.html
>>
>> On Tue, Apr 11, 2017 at 8:36 AM, Andrew Harris <aharr8 at uwo.ca> wrote:
>>
>>> In the image mask, the parts we want to include in the calculation are
>>> bright and the parts that we want to exclude are dark, is that the opposite
>>> of what it should be?
>>>
>>> --
>>>
>>> AH
>>>
>>>
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>>> On Thu, Mar 30, 2017 at 7:25 PM, Dženan Zukić <dzenanz at gmail.com> wrote:
>>>
>>>> Hi Andrew,
>>>>
>>>> your masks might be inverted. If NCC gets all black pixels in both
>>>> images, the correlation will be perfect.
>>>>
>>>> Regards,
>>>> Dženan
>>>>
>>>> On Thu, Mar 30, 2017 at 2:13 PM, Andrew Harris <aharr8 at uwo.ca> wrote:
>>>>
>>>>> Hi, I'm hoping someone can guide me toward an explanation of this.  I
>>>>> run my pipeline on various ultrasound image sets and get an NCC between
>>>>> 0.65 and 0.8 for good alignments, but on some sets the NCC returns 1.0 when
>>>>> the images aren't even overlapping.  I have the black areas of the image
>>>>> masked out, and have even tried cranking up the threshold to be sure the
>>>>> darker areas aren't being included to no avail.  Any thoughts?
>>>>>
>>>>> --
>>>>>
>>>>> AH
>>>>>
>>>>>
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