Clustering of an image by taking into account the spatial context of each pixel (besides its intensity)

Juan Nunez-Iglesias jni.soma at
Fri Nov 20 19:46:58 EST 2015

Hey Hakim,

The right answer here depends on your ultimate goal. If you're after
denoising, non-local means denoising (recently added to skimage) sounds
like exactly what you're after.


On Sat, Nov 21, 2015 at 11:28 AM, Hakim Benoudjit <h.benoudjit at>

> Hi Stéfan,
> Thanks for your reponse.
> What I'm looking for is a *spatial criteria* that encourages the *clustering
> algorithm* (K-means or others) to group together similar *neighbouring
> pixels* inside the same cluster. This will help avoid having persistent
> noise inside a cluster.
> Le vendredi 20 novembre 2015 13:20:15 UTC, Hakim Benoudjit a écrit :
>> Hi,
>> Is there a clustering algorithm implemented in *scikit-image *that
>> perform the image clustering by taking into account the *spatial context
>> *of the clustered pixel (its neighbourhood), besides its *pixel
>> brightness*?
>> For the time being, I'm clustering images by reshaping them as vectors of
>> pixels intensities distributions, and then performing the *K-means *or *Gaussian
>> mixture models* implemented in *scikit-learn*. But, I'm looking for a
>> image clustering technique implemented (or could be implemented) in *scikit-image
>> *that would consider the neighbourhood of a pixel when classifying it.
>> Thanks.
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