Finding pairs of images (homologous chromosomes)

[Jean-Patrick Pommier]

Dear All,

I am trying to make pairs of images from the following set of images (chromosomes sorted by size after rotation). The idea is to make a feature vector for unsupervised classification (kmeans with 19 clusters)


From each chromosome an integral image was calculated:

plt.figure(figsize = (15,15))
gs1 = gridspec.GridSpec(6,8)
gs1.update(wspace=0.0, hspace=0.0) # set the spacing between axes.
for i in range(38):
   # i = i + 1 # grid spec indexes from 0
    ax1 = plt.subplot(gs1[i])
    plt.axis('off')
    ax1.set_xticklabels([])
    ax1.set_yticklabels([])
    ax1.set_aspect('equal')
    image = sk.transform.integral_image(reallysorted[i][:,:,2])
    imshow(image , interpolation='nearest')

Then each integral image was flatten and combined with the others:

Features =[]

for i in range(38):
    Feat = np.ndarray.flatten(sk.transform.integral_image(reallysorted[i][:,:,2]))
    Features.append(Feat)
X = np.asarray(Features)
print X.shape

The X array contains 38 lines and 9718 features, which is not good. However, I trried to submit these raw features to kmeans classification with sklearn using a direct example :

from sklearn.neighbors import NearestNeighbors
nbrs = NearestNeighbors(n_neighbors=19, algorithm='ball_tree').fit(X)
distances, indices = nbrs.kneighbors(X)
connection = nbrs.kneighbors_graph(X).toarray()

Ploting the connection graph shows that a chromosomes is similar to more than one ...

• Do you think that integral images can be used to discriminate the chromosomes pairs?
• If so, how to reduce the number of features to 10~20? (to get a better discrimination)
  

Thanks for your advices.

Jean-Patrick

[Josh Warner]

Neat problem!

For feature extraction, `skimage.feature` is probably your friend. Nothing against integral images, but I'm not sure they are going to give you an ideal feature set for discrimination (you can see that visually). Also, attempting to normalize your input data might be worth looking into at some point as it appears exposure is not uniform.

As a first pass, you could feed raw grayscale values straight into e.g. a Bernoulli Restricted Boltzmann machine, or check out scikit-learn's excellent tutorial on digit recognition. Though for both of those options, the performance is going to be strongly dependent on the quality and - especially - quantity of the training set.

Beyond that, thresholding and a skeletonization with `skimage.morphology.skeletonize` might give you informative morphology data to feed in to a classifier. 

Best of luck,

Josh

[Jean-Patrick Pommier]

Thanks you for the links.

Regarding the rbm classifier in the following example. At first sight I don't understand what is Y array (X array seems to be the set of images).


Jean-Patrick

[Josh Warner]

Hi Jean-Patrick,

Y is the known corresponding digit identity. The function to "jitter" the digit images around a bit just takes digits.target as Y and concatenates it with itself five times, so the expanded dataset has known identities to compare against.

Regards,

Josh

[Stefan van der Walt]

Hi Jean-Patrick

On 2015-02-24 08:21:10, Jean-Patrick Pommier <jeanpatri...@gmail.com> wrote:
> I am trying to make pairs of images from the following set of images
> (chromosomes sorted by size after rotation). The idea is to make a feature
> vector for unsupervised classification (kmeans with 19 clusters)

This is a *very* late reply, but I thought I'd mention that François
Boulogne & Gaël Varoquaux has included a digit classifier in the
skimage-demos repository, which may be helpful.

Best regards
Stéfan

[Jean-Patrick Pommier]

Thank you anyway,

Jean-pat

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