Apply segmentation to a large binary image

[Yuta Sato]

Thanks Juan Nunez-Iglesias for the information!



On Sat, Apr 4, 2015 at 10:02 PM, Juan Nunez-Iglesias <jni.soma at gmail.com>
wrote:

> Hey Yuta,
>
> I'm not sure how much h5py mirrors the numpy array interface, but I
> suspect not sufficiently to allow C/Cython functions to work. I think
> you'll need to find a clever way to partition your image to get the right
> result, because although you can use numpy.memmap [1] to use an on-disk
> array, I think that might be far too slow (since binary fill holes uses
> quite a few iterations...
>
> Juan.
>
> [1] http://docs.scipy.org/doc/numpy/reference/generated/numpy.memmap.html
>
>
>
>
> On Sat, Apr 4, 2015 at 12:54 AM, Yuta Sato <yutaxsato at gmail.com> wrote:
>
>>     Dear Juan Nunez-Iglesias and Josh Warner:
>>
>>  Thanks for your kind responses.
>>  Lets take a more simpler case, e.g., binary_fill_holes.
>>  I want to apply to the WHOLE image at once, because if I apply it in
>> the parts of image and later combine, the result differs.
>>
>>  Does putting the image into hdf5 and applying the binary_fill_holes
>> solve my problem?
>>  Can I really apply binary_fill_holes to hdf5 file?
>>
>>  Thanks for your helps.
>>
>>  Yuta
>>
>>
>>
>>
>>
>> On Sun, Mar 15, 2015 at 10:18 AM, Juan Nunez-Iglesias <jni.soma at gmail.com
>> > wrote:
>>
>>> Josh, you might be thinking of morphology.remove_small_objects, but that
>>> is O(image.size), rather than O(sum(image == label)), which is what you are
>>> after. In fact we would need a flood-fill algorithm, which we don't have...
>>> That would be a fantastic addition.
>>>
>>>
>>>
>>>
>>> On Sun, Mar 15, 2015 at 10:24 AM, Josh Warner <
>>> silvertrumpet999 at gmail.com> wrote:
>>>
>>>> Would it be possible to generalize / refactor `clear_border` to a
>>>> function which removes all points connected to a specific pixel/voxel? That
>>>> would greatly simplify the work needed here.
>>>>
>>>> I thought we had some sort of `remove_object` functionality like this,
>>>> but I don't see it.
>>>>
>>>> Josh
>>>>
>>>> On Friday, March 13, 2015 at 9:04:12 PM UTC-5, Juan Nunez-Iglesias
>>>> wrote:
>>>>>
>>>>> Hey Yuta,
>>>>>
>>>>> You'll need to do some stitching out-of-core. That's a really tricky
>>>>> problem and I don't have any ready-made solutions for you. The solution
>>>>> will depend on the nature of your segments. The only thing I would
>>>>> recommend is that you use a format such as HDF5 (you can use the excellent
>>>>> h5py library) that allows random access into the underlying disk data.
>>>>>
>>>>> Other than that, as I said, to my knowledge you'll have to develop
>>>>> your own stitching: segment *overlapping* tiles independently in memory,
>>>>> and when it comes time to write to disk, load the tile and overlapping
>>>>> tiles that have already been segmented, and resolve label mapping then...
>>>>>
>>>>> Generally, think of it this way: tile i has already been segmented and
>>>>> written out. We now want to write out tile j, which overlaps tile i. Labels
>>>>> from tile i that intersect labels from tile j in the overlap region should
>>>>> be matched. labels in tile j that *don't* intersect tile i should be
>>>>> relabelled to ensure they are unique with respect to tile i.
>>>>>
>>>>> Of course this gets a bit more complicated in 2D or 3D...
>>>>>
>>>>> Juan.
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Fri, Mar 13, 2015 at 7:20 PM, Yuta Sato <yutaxsato at gmail.com>
>>>>> wrote:
>>>>>
>>>>>>  Dear SKIMAGE Developers and Users:
>>>>>>
>>>>>> I want to use the following algorithm in a large binary image that
>>>>>> does not fit into my PC memory. So, I am thinking to split my large image
>>>>>> into tiles and apply algorithm one by one. However, the original border
>>>>>> definition change when I do it in parts. I need the result as applied in
>>>>>> original full image. How can I do it efficiently?
>>>>>>
>>>>>> skimage.segmentation.clear_border(image, buffer_size=0, bgval=0)
>>>>>>
>>>>>> Thanks for your ideas.
>>>>>>
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>>>>>
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