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. Juan. On Sat, Nov 21, 2015 at 11:28 AM, Hakim Benoudjit <h.benoudjit@gmail.com> wrote:

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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