[Insight-users] Image boundary solutions

[Bill Lorensen]

This example illustrates the confidence connected filter (in 2D):
http://itk.org/Wiki/ITK/Examples/ImageSegmentation/ConfidenceConnectedImageFilter



On Tue, Aug 6, 2013 at 1:47 PM, Matt McCormick
<matt.mccormick at kitware.com>wrote:

> Hi Jesse,
>
> The ConfidenceConnectedImageFilter[1] or other region growing algorithms
> described in the ITK Software Guide [2] will likely do what you desire.
>
> Thanks,
> Matt
>
> [1]
> http://www.itk.org/Doxygen/html/classitk_1_1ConfidenceConnectedImageFilter.html
> [2] http://www.itk.org/ItkSoftwareGuide.pdf
>
> Fri, Jun 7, 2013 at 8:08 AM, Jesse Ross-Jones <jesse.rj at gmail.com> wrote:
>
>> Hi,
>> So far I have not been able to find a function that does what the
>> description describes. The closest was PadByRadius from ImageRegion,
>> however this pads uniformly in any one dimension.
>>
>> Therefore I started to think about the steps necessary to recreate the
>> method. This is what I have come up with so far, I would appreciate any
>> feedback you might have.
>>
>> 0. Original image of the eye (original.im)
>> 1. Threshold image to create binary image (binary.im) (0 =
>> outside/background, 1 = inside field of view)
>> 2. Dilate binary image by 1 pixel (dilated.im)
>> 3. Subtract binary image from dilated image (dilated.im - binary.im)
>> this creates a one pixel width boarder around the field of view FOV (
>> boarder.im)
>> 4. For every pixel (i) = 1 in the boarder image find the 8 neighborhood
>> pixels in both the binary image (binary.im) and the original (original.im)
>> and mutliply each corresponding neighborhood pixel together. This leaves
>> only the grey-values of the pixels inside the FOV and removes the
>> grey-values of the background.
>> 5. Find the average of these multiplied 8 pixels and assign this average
>> to the current pixel (i),
>> 6. Repeat for remaining pixels inside the boarder
>>
>> The whole process is then repeated until a large enough boarder is
>> created around the FOV of the eye.
>>
>> Can this be done more efficiently or simply?
>>
>> I look forward to thoughts on this,
>> much appreciated,
>> Jesse
>>
>>
>> On Wed, Jun 5, 2013 at 1:19 PM, Jesse Ross-Jones <jesse.rj at gmail.com>wrote:
>>
>>> Dear ITK Users and Devs,
>>>
>>> I am working on vessel segmentation, using the STARE and DRIVE image
>>> datasets, and in order to deal with the image boundary I am trying to
>>> implement the following:
>>>
>>> The preprocessing algorithm starts with a region of interest
>>> (ROI) determined by the camera’s aperture and iteratively grows
>>> this ROI. Each step of the algorithm consists in the following.
>>> First, the set of pixels of the exterior border of the ROI is de-
>>> termined, i.e., pixels that are outside the ROI and are neighbors
>>> (using four-neighborhood) to pixels inside it. Then, each pixel
>>> value of this set is replaced with the mean value of its neighbors
>>> (this time using eight-neighborhood) inside the ROI. Finally, the
>>> ROI is expanded by inclusion of this altered set of pixels. This
>>> process is repeated and can be seen as artificially increasing the
>>> ROI, as shown in Fig. 1(b).
>>>
>>> from http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01677727&tag=1
>>>
>>> Is there something similar already implemented? I am considering instead
>>> to use the Wrap Pad Image Filter:
>>> http://www.itk.org/Doxygen/html/classitk_1_1WrapPadImageFilter.html
>>> to pad the region around the eye. Would this be a better possibility?
>>>
>>> Much Appreciated,
>>> Jesse
>>>
>>>
>>
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>
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