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Commit 825fb065 authored by monj's avatar monj
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Added forms for visualising the importance of the clusters

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code/lung_feature_patch_allPatients.py
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......@@ -16,20 +16,20 @@ Extended functionality by Monica J. Emerson.
- Study of several diseases.
- Added functionality for supporting the analysis of different data versions.
- Computation of health scores per image, sample and patient.
- Visualisation of cluster centres as a grid.
- Visualisation of cluster centers as a grid.
- Boxplots to compare probabilities across samples and to clinical values.
- Normalisation of intensities across images and channels.
- Normalisation of int_sum_list across images and channels.
- Possibility to ignore the background (air phase).
- Visualisation of assignment images to inspect results and support the development of the approach to ignore background.
- Implementation and investigation of feature variations (colour, bnw, bnw+colour).
- Study of the parameters (nr clusters and scale - relative patch/image size)
- Extended visualisation of cluster centres to support the comparison across diseases and parameters.
a) Visualisation of cluster centres split into channels.
- Extended visualisation of cluster centers to support the comparison across diseases and parameters.
a) Visualisation of cluster centers split into channels.
b) Compute a population (p) and condition probability (c) value for each cluster.
c) Identify the presence of "weak" clusters. If they exist, rerun kmeans.
d) Select and visualise characteristic clusters for the conditions based on p and c.
e) Plot all clusters in the population/condition probability space.
f) Order cluster centres according to condition probability
f) Order cluster centers according to condition probability
"""
import numpy as np
......@@ -43,6 +43,7 @@ import os
from datetime import datetime
import sys
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from math import ceil
startTime = datetime.now()
......@@ -51,14 +52,15 @@ plt.close('all')
sc_fac = 0.25 #25 #25 #0.5 # scaling factor of the image
patch_size = 17
nr_clusters = 100 # number of clusters
colour_mode = 'colour' #'bnw' #colour
#%% Directories
version = 'corrected_bis'
preprocessing = '' #'/preprocessed_ignback/' #''(none)
colour_mode = 'colour' #'bnw' #colour
disease = ['sarcoidosis']#diseased (mix, 2 of each condition)' #''emphysema' 'sarcoidosis'
preprocessing = '/preprocessed_ignback/' #''(none)
disease = 'diseased' #diseased (mix, 2 of each condition)' #''emphysema' 'sarcoidosis'
conditions = [disease]
conditions.insert(0,'control')
# input directories - images start with the name 'frame'
dir_in = '../maxProjImages_'+version + preprocessing
......@@ -66,16 +68,15 @@ dir_in = '../maxProjImages_'+version + preprocessing
# dir_sick = dir_in + disease + '/' #191216_100a/'
base_name = 'frame'
#output directories
dir_results = '../results_monj/patches/data_' + version+'/' #rerun just to make sure not to muck up the results
os.makedirs(dir_results, exist_ok = True)
dir_probs = dir_results + disease + '_' + colour_mode + '_%dclusters_%ddownscale_%dpatchsize/'%(nr_clusters,1/sc_fac,patch_size)
ma.make_output_dirs(dir_probs, disease)
ma.make_output_dirs(dir_probs, conditions)
dir_probs_withBack = dir_probs + 'withBackground'
ma.make_output_dirs(dir_probs_withBack, disease)
dir_probs_noBack = dir_probs + 'noBackground/'
ma.make_output_dirs(dir_probs_noBack, conditions)
# if not os.path.exists(dir_probs):
# os.mkdir(dir_probs)
......@@ -91,38 +92,39 @@ print('Reading maximum projection images')
max_img_list_control = ma.read_max_imgs(dir_in + 'control', base_name)
max_img_list_sick = ma.read_max_imgs(dir_in + disease, base_name)
#TO DO: Consider removing colour mode and rescaling from the read function.
# max_im_list_control = ma.read_max_ims(dir_in_max + 'control/' ,base_name,sc_fac,colour_mode)
#%% Prepare maximum projection images for feature extraction
# max_im_list_sick = []
# for sample in dir_list_sick:
# in_dir = dir_sick + sample + '/'
# dir_list = [dI for dI in sorted(os.listdir(in_dir)) if dI[0:len(base_name)]==base_name]
# frames_list = []
# for ind, frame in enumerate(dir_list):
# frame_path = in_dir + frame
#Flatten and merge conditions
max_img_list_control_flat = ma.flatten_list(max_img_list_control)
max_img_list_sick_flat = ma.flatten_list(max_img_list_sick)
#Rescale
max_img_list_control_processed = []
for max_img in ma.flatten_list(max_img_list_control) :
# if colour_mode == 'bnw':
# img = skimage.color.rgb2gray(skimage.io.imread(frame_path).astype(float))
# max_im_list_sick += [skimage.transform.rescale(img, sc_fac)]
# max_img_list_flat_processed += [skimage.transform.rescale(skimage.color.rgb2gray(max_img), sc_fac, preserve_range = True).astype('uint8')]
# else:
# img = skimage.io.imread(frame_path).astype(float)
# max_im_list_sick += [skimage.transform.rescale(img, sc_fac, multichannel=True)]
max_img_list_control_processed += [skimage.transform.rescale(max_img, sc_fac, preserve_range = True, multichannel=True).astype('uint8')]
#TO DO: Rescale max proj. images, overwrite original variables
#TO DO: Compute bnw version, but keep the colour one for displaying it at the end
max_img_list_sick_processed = []
for max_img in ma.flatten_list(max_img_list_sick) :
max_img_list_sick_processed += [skimage.transform.rescale(max_img, sc_fac, preserve_range = True, multichannel=True).astype('uint8')]
#%% Compute patches
patch_feat_list_control = []
for max_im in max_im_list_control:
patch_feat_list_control += [ma.ndim2col_pad(max_im, (patch_size, patch_size),norm=False).transpose()]
for max_img in max_img_list_control_processed:
patch_feat_list_control += [ma.ndim2col_pad(max_img, (patch_size, patch_size),norm=False).transpose()]
patch_feat_list_sick = []
for max_im in max_im_list_sick:
patch_feat_list_sick += [ma.ndim2col_pad(max_im, (patch_size, patch_size),norm=False).transpose()]
for max_img in max_img_list_sick_processed:
patch_feat_list_sick += [ma.ndim2col_pad(max_img, (patch_size, patch_size),norm=False).transpose()]
patch_feat_total = patch_feat_list_control + patch_feat_list_sick
patch_feat_total = []
patch_feat_total += patch_feat_list_control
patch_feat_total += patch_feat_list_sick
#TO DO: Consider turning patches to bnw here instead
#so we can read the protein content for each patch as the sum of the patch int_sum_list
#%% features for clustering
nr_keep = 10000 # number of features randomly picked for clustering
......@@ -137,101 +139,155 @@ for patch_feat in patch_feat_total:
#%% k-means clustering
batch_size = 1000
th_nr_pathesINcluster = 5
th_nr_pathesINcluster = 10
if os.path.exists(dir_probs + 'array_cluster_centres'+colour_mode+'.npy'):
cluster_centres = np.load(dir_probs + 'array_cluster_centres'+colour_mode+'.npy')
#kmeans = sklearn.cluster.MiniBatchKMeans(n_clusters=nr_clusters, init = cluster_centres, batch_size = batch_size)
#If cluster centers were already computed, use those
if os.path.exists(dir_probs + 'array_cluster_centers_'+colour_mode+'.npy'):
cluster_centers = np.load(dir_probs + 'array_cluster_centers_'+colour_mode+'.npy')
#kmeans = sklearn.cluster.MiniBatchKMeans(n_clusters=nr_clusters, init = cluster_centers, batch_size = batch_size)
#kmeans.fit(patch_feat_to_cluster)
kmeans = sklearn.cluster.MiniBatchKMeans(n_clusters=nr_clusters, batch_size = batch_size)
kmeans.cluster_centers_=cluster_centres
reusing_clusters = True
kmeans.cluster_centers_ = cluster_centers
#Compute cluster centers and save if there is no weak clusters
else:
kmeans = sklearn.cluster.MiniBatchKMeans(n_clusters=nr_clusters, batch_size = batch_size)
kmeans.fit(patch_feat_to_cluster)
all_cluster_centres = kmeans.cluster_centers_
#Cluster statistics
#Nr. patches that have contributed to each cluster
features_in_cluster = []
for cluster in range(0,nr_clusters):
features_in_cluster += [[ind for ind,i in enumerate(kmeans.labels_) if i==cluster]]
nr_feat_in_cluster = [len(i) for i in features_in_cluster]
#Weak clusters are those composed by very few patches
nr_weakClusters = len([1 for i in nr_feat_in_cluster if i<th_nr_pathesINcluster])
#If there are weak clusters, the clustering should be recomputed
if nr_weakClusters!=0:
sys.exit(str(nr_weakClusters)+ " clusters composed of less than "+str(th_nr_pathesINcluster)+" images")
else:
np.save(dir_probs + 'array_cluster_centres'+colour_mode+'.npy', all_cluster_centres) # .npy extension is added if not given
#%% Read background pixels
dir_background = dir_in + 'background/'
dir_background_list = [dI for dI in os.listdir(dir_background) if os.path.isdir(os.path.join(dir_background,dI))]
fig, axs = plt.subplots(2,len(dir_background_list), sharex=True, sharey=True)
patch_feat_back = []
for ind, directory in enumerate(dir_background_list):
#load images and corresponding background labels
file_names = [f for f in os.listdir(dir_background +directory) if f.endswith('.png')]
im_file = [f for f in file_names if not f.startswith('back')].pop()
label_file = [f for f in file_names if f.startswith('back')].pop()
im_back = skimage.io.imread(dir_background + directory + '/' + im_file).astype('uint8')
label_back = skimage.color.rgb2gray(skimage.io.imread(dir_background + directory + '/' + label_file).astype('float'))
label_back += -np.min(label_back)
label_back = label_back.astype('bool')
#plot imagesand corresonding labels
axs[0][ind].imshow(im_back)
axs[1][ind].imshow(label_back,'gray')
plt.show()
#compute features
if colour_mode == 'bnw':
im_back = skimage.transform.rescale(skimage.color.rgb2gray(im_back.astype(float)), sc_fac, multichannel=False)
else :
im_back = skimage.transform.rescale(im_back.astype(float), sc_fac, multichannel=True)
im_feat = ma.ndim2col_pad(im_back, (patch_size, patch_size)).transpose()
patch_feat_back += [im_feat[(skimage.transform.rescale(label_back, sc_fac, multichannel=False)==True).ravel(),:]]
np.save(dir_probs + 'array_cluster_centers_'+colour_mode+'.npy', kmeans.cluster_centers_) # .npy extension is added if not given
#%% Plot all cluster centres
#%% Plot all cluster centers (grid view)
plot_grid_cluster_centres(kmeans.cluster_centers_)
#grid dimensions
size_x = round(nr_clusters**(1/2))
size_y = ceil(nr_clusters/size_x)
if nr_clusters==100:
fig, axs = plt.subplots(10,10, figsize=(5,5), sharex=True, sharey=True)
if nr_clusters==200:
w, h = plt.figaspect(2.)
fig, axs = plt.subplots(20,10, figsize=(w,h), sharex=True, sharey=True)
if nr_clusters==1000:
fig, axs = plt.subplots(100,100, figsize=(5,5), sharex=True, sharey=True)
#figure format
w, h = plt.figaspect(size_x/size_y)
fig, axs = plt.subplots(size_x,size_y, figsize=(w,h), sharex=True, sharey=True)
intensities = []
for ax, cluster_nr in zip(axs.ravel(), np.arange(0,nr_clusters)):
if colour_mode == 'bnw':
cluster_centre = np.reshape(kmeans.cluster_centers_[cluster_nr,:],(patch_size,patch_size))
intensities += [sum((cluster_centre).ravel())]
ax.imshow(cluster_centre.astype('uint8'),cmap='gray')
print('Grid size: ', size_x, size_y, 'Figure size: ', w, h)
#plot cluster centers
int_sum_list = []
ax_list = axs.ravel()
for ind in np.arange(0,nr_clusters):
if colour_mode == 'bnw': #in bnw + colour give the clusters a uniform colour
cluster_centre = np.reshape(kmeans.cluster_centers_[ind,:],(patch_size,patch_size))
int_sum_list += [sum((cluster_centre).ravel())]
ax_list[ind].imshow(cluster_centre.astype('uint8'),cmap='gray')
else:
cluster_centre = np.transpose((np.reshape(kmeans.cluster_centers_[cluster_nr,:],(3,patch_size,patch_size))),(1,2,0))
intensities += [sum((np.max(cluster_centre,2)).ravel())]
ax.imshow(cluster_centre.astype('uint8'))
cluster_centre = np.transpose((np.reshape(kmeans.cluster_centers_[ind,:],(3,patch_size,patch_size))),(1,2,0))
int_sum_list += [sum((np.max(cluster_centre,2)).ravel())]
ax_list[ind].imshow(cluster_centre.astype('uint8'))
plt.setp(axs, xticks=[], yticks=[])
plt.savefig(dir_probs + 'clusterCentres_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
plt.savefig(dir_probs + 'clustercenters_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
#%% Histograms for background, healthy and sick
#hist_control, assignment_list_control, hist_back, assignment_back = ma.compute_assignment_hist(patch_feat_list_control, kmeans, background_feat=im_feat_back)
hist_background, assignment_list_background = ma.compute_assignment_hist(patch_feat_back, kmeans)
hist_control, assignment_list_control= ma.compute_assignment_hist(patch_feat_list_control, kmeans)
hist_sick, assignment_list_sick = ma.compute_assignment_hist(patch_feat_list_sick, kmeans)
#%% Cluster centres in the 2d space determined by the relationshop between histogram
occurrence_ratio = hist_control/hist_sick
occurrence_ratio[occurrence_ratio<1] = -1/occurrence_ratio[occurrence_ratio<1]
#%% show bar plot of healthy and sick
fig, ax = plt.subplots(1,1)
ax.bar(np.array(range(0,nr_clusters))-0.25, hist_control, width = 0.5, label='Control', color = 'r')
ax.bar(np.array(range(0,nr_clusters))+0.25, hist_sick, width = 0.5, label='Sick', color = 'b')
ax.legend()
plt.savefig(dir_probs + 'assignmentHistograms_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
#%% Compute populated, occurrence and importance cluster measures from the histogram
#OCCURRENCE RATIO: How more often does it occur in one of the conditions
occurrence_ratio = np.empty((nr_clusters))
ind_control = hist_control>hist_sick
ind_sick = ~ind_control
control_cond = (hist_sick!=0) & ind_control
occurrence_ratio[control_cond] = hist_control[control_cond]/hist_sick[control_cond]
sick_cond = (hist_control!=0) & ind_sick
occurrence_ratio[sick_cond] = -hist_sick[sick_cond]/hist_control[sick_cond]
#POPULATED How trustworthy is the cluster?
populated = hist_control+hist_sick
plt.hist2d(occurrence_ratio,populated)
weights = populated/populated.max()
#Cluster IMPORTANCE: occurrence weighed by POPULATED
importance = ((2*np.exp(populated/populated.max())-1)*occurrence_ratio).reshape((size_x,size_y))
importance[importance>2*importance.std()] = 2*importance.std()
importance[importance<-2*importance.std()] = -2*importance.std()
#%% Plot measures as images
#OCCURRENCE RATIO
fig, ax = plt.subplots(figsize=(5,5))
plt.imshow(occurrence_ratio.reshape((size_x,size_y)),cmap = 'PiYG')
plt.colorbar()
plt.setp(axs, xticks=[], yticks=[])
plt.title('Condition dominance \n Dominant condition (+ control, - disease)')
#POPULATED
fig, ax = plt.subplots(figsize=(5,5))
plt.imshow(populated.reshape((size_x,size_y)),cmap = 'PiYG')
plt.colorbar()
plt.setp(axs, xticks=[], yticks=[])
plt.title('Cluster population ratio \n Distribution of patches across clusters')
#Cluster IMPORTANCE: occurrence times POPULATED
fig, ax = plt.subplots(figsize=(5,5))
plt.imshow((populated*occurrence_ratio).reshape((size_x,size_y)),cmap = 'PiYG')
plt.colorbar()
plt.setp(ax, xticks=[], yticks=[])
plt.title('Cluster importance \n Occurrence ratio*population')
#Saturated cluster IMPORTANCE: occurrence times POPULATED Saturated
fig, ax = plt.subplots(figsize=(5,5))
plt.imshow(importance, cmap = 'PiYG', vmin=-2*importance.std(), vmax=2*importance.std())
colorbar = plt.colorbar()
limit_cmap = max(occurrence_ratio)
ticks = np.linspace(-limit_cmap,limit_cmap,len(colorbar.get_ticks()))
tick_labels = [str(i) for i in ticks.tolist()]
colorbar.set_ticklabels(np.linspace(-limit_cmap,limit_cmap,len(colorbar.get_ticks())))
plt.setp(ax, xticks=[], yticks=[])
plt.title('Cluster importance for the condition \n Healthy (green), ' + str(disease) + ' (pink)')
plt.savefig(dir_probs + 'clusterCentreImportance_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
#%% Plot grid of ordered clusters
#Order the clusters according to importance
clusters_control_importance_sorted = [np.arange(0,100)[i] for i in np.argsort(importance.flatten()) if (importance.flatten())[i]>0]
clusters_control_importance_sorted.reverse()
clusters_sick_importance_sorted = [np.arange(0,100)[i] for i in np.argsort(importance.flatten()) if (importance.flatten())[i]<0]
clusters_importance_sorted = [np.arange(0,100)[i] for i in np.argsort(importance.flatten())]
clusters_importance_sorted.reverse()
occurrence_ratio_sorted = [occurrence_ratio[i] for i in np.argsort(importance.flatten()) ]
occurrence_ratio_sorted.reverse()
ma.plot_grid_cluster_centers(kmeans.cluster_centers_, clusters_importance_sorted, patch_size, colour_mode = 'colour', occurrence = occurrence_ratio_sorted)
plt.suptitle('Cluster centers \n from more characteristic of control (0, ' + str(len(clusters_control_importance_sorted)) + ') to more characteristic of sick')
plt.savefig(dir_probs + 'clustercenters_sorted_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
#%% Plot cluster centers in the 2d populated/occurrence space
fig_control, ax_control = plt.subplots(figsize=(15,15))
ax_control.scatter(populated[occurrence_ratio>1], occurrence_ratio[occurrence_ratio>1])
......@@ -254,74 +310,82 @@ for x0, y0, cluster_nr in zip(populated, occurrence_ratio, np.arange(0,nr_cluste
else:
ab = AnnotationBbox(OffsetImage(cluster_centre.astype('uint8')), (x0, y0), frameon=False)
ax_control.add_artist(ab)
plt.savefig(dir_probs + 'controlClusterCentres_2Dspace_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
plt.savefig(dir_probs + 'controlClustercenters_2Dspace_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
else:
if colour_mode == 'bnw':
ab = AnnotationBbox(OffsetImage(cluster_centre.astype('uint8'),cmap='gray'), (x0, -y0), frameon=False)
else:
ab = AnnotationBbox(OffsetImage(cluster_centre.astype('uint8')), (x0, -y0), frameon=False)
ax_sick.add_artist(ab)
plt.savefig(dir_probs + 'sickClusterCentres_2Dspace_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
plt.savefig(dir_probs + 'sickClustercenters_2Dspace_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
#%% Find background clusters
th_back = 10000
#background clusters
clusters_background_intBased = [i for i in range(len(int_sum_list)) if int_sum_list[i] < 8000]
#clusters_background_annotBased = [ind for ind, value in enumerate(hist_background) if value>0]
#%% show bar plot of healthy and sick
clusters_background = clusters_background_intBased
#clusters_background = list(set(clusters_background_intBased) | set(clusters_background_annotBased))
print('Background clusters'+str(clusters_background))
fig, ax = plt.subplots(1,1)
ax.bar(np.array(range(0,nr_clusters)), hist_background, width = 1)
plt.show()
plt.savefig(dir_probs + 'backgroundHistogram_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
#%% Plot grid of ordered, non-background clusters that are populated over a thresh
th_populated = 0.4/nr_clusters
th_occurr = 1.25
fig, ax = plt.subplots(1,1)
ax.bar(np.array(range(0,nr_clusters))-0.25, hist_control, width = 0.5, label='Control', color = 'r')
ax.bar(np.array(range(0,nr_clusters))+0.25, hist_sick, width = 0.5, label='Sick', color = 'b')
ax.legend()
plt.savefig(dir_probs + 'assignmentHistograms_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
#control
clusters_control_importance_sorted_noBackground = [i for i in clusters_control_importance_sorted if (i not in clusters_background)&(populated[i]>th_populated)&(occurrence_ratio[i]>th_occurr)]
ma.plot_grid_cluster_centers(kmeans.cluster_centers_, clusters_control_importance_sorted_noBackground, patch_size, colour_mode = 'colour')
#%% Find background and characteristic clusters
plt.suptitle('Characteristic clusters for control, excluding background, \n from most important to least')
plt.savefig(dir_probs_noBack + 'clustercenters_control_sorted_noBack%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
#background clusters
clusters_background_intBased = [i for i in range(len(intensities)) if intensities[i] < 7000]
clusters_background_annotBased = [ind for ind, value in enumerate(hist_background) if value>0]
#disease
clusters_sick_importance_sorted_noBackground = [i for i in clusters_sick_importance_sorted if (i not in clusters_background)&(populated[i]>th_populated)&(abs(occurrence_ratio[i])>th_occurr)]
clusters_background = list(set(clusters_background_intBased) | set(clusters_background_annotBased))
print('Background clusters'+str(clusters_background))
ma.plot_grid_cluster_centers(kmeans.cluster_centers_, clusters_sick_importance_sorted_noBackground, patch_size, colour_mode = 'colour')
#characteristic clusters
th_proportion = 2#2 #2.4
th_populated = 0.01#0.005#0.015
plt.suptitle('Charactertistic clusters for ' + disease + ', excluding background, \n from most important to least')
plt.savefig(dir_probs_noBack + 'clustercenters_sick_sorted_noBack%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
clusters_sick = (hist_sick>th_populated)&(hist_sick>th_proportion*hist_control)
clusters_sick = [ind for ind,value in enumerate(clusters_sick) if value == True]
clusters_control = (hist_control>th_populated)&(hist_control>th_proportion*hist_sick)
clusters_control = [ind for ind,value in enumerate(clusters_control) if value == True]
# #%% Find characteristic clusters
# th_proportion = 2#2 #2.4
# th_populated_condition = 0.01#0.005#0.015
#eliminate backgorund clusters if contained here
clusters_control = [i for i in clusters_control if i not in clusters_background]
clusters_sick = [i for i in clusters_sick if i not in clusters_background]
# clusters_sick = (hist_sick>th_populated_condition)&(hist_sick>th_proportion*hist_control)
# clusters_sick = [ind for ind,value in enumerate(clusters_sick) if value == True]
# clusters_control = (hist_control>th_populated_condition)&(hist_control>th_proportion*hist_sick)
# clusters_control = [ind for ind,value in enumerate(clusters_control) if value == True]
print('Clusters characteristic of the ' + disease + ' tissue',clusters_sick)
print('Clusters characteristic of the control tissue',clusters_control)
# #eliminate background clusters if contained here
# clusters_control = [i for i in clusters_control if i not in clusters_background]
# clusters_sick = [i for i in clusters_sick if i not in clusters_background]
#%% Plot centres of the characteristic clusters
cluster_centres_control = kmeans.cluster_centers_[clusters_control]
# print('Clusters characteristic of the ' + disease + ' tissue',clusters_sick)
# print('Clusters characteristic of the control tissue',clusters_control)
#%% Plot centers of the characteristic clusters
nr_clusters_display = 10
clusters_control = clusters_control_importance_sorted_noBackground[1:10]
#control clusters and contrast enhanced
cluster_centres_control = kmeans.cluster_centers_[clusters_control]
cluster_centers_control = kmeans.cluster_centers_[clusters_control]
fig, axs = plt.subplots(1,len(clusters_control), figsize=(len(clusters_control)*3,3), sharex=True, sharey=True)
fig.suptitle('Cluster centres for control')
fig.suptitle('Cluster centers for control')
if colour_mode!='bnw':
fig_split, axs_split = plt.subplots(3,len(clusters_control), figsize=(len(clusters_control)*3,9), sharex=True, sharey=True)
fig_split.suptitle('Control centres split channels (contrast enhanced)')
fig_split.suptitle('Control centers split channels (contrast enhanced)')
for l in np.arange(0,len(clusters_control)):
if colour_mode == 'bnw':
cluster_centre = np.reshape(cluster_centres_control[l,:],(patch_size,patch_size))
cluster_centre = np.reshape(cluster_centers_control[l,:],(patch_size,patch_size))
axs[l].imshow(cluster_centre.astype(np.uint8),cmap='gray')
axs[l].axis('off')
axs[l].set_title(clusters_control[l])
else:
cluster_centre = np.transpose((np.reshape(cluster_centres_control[l,:],(3,patch_size,patch_size))),(1,2,0))
cluster_centre = np.transpose((np.reshape(cluster_centers_control[l,:],(3,patch_size,patch_size))),(1,2,0))
axs_split[0][l].imshow(cluster_centre[...,0].astype(np.uint8),cmap='gray')
axs_split[0][l].axis('off')
axs_split[0][l].set_title(clusters_control[l])
......@@ -330,28 +394,30 @@ for l in np.arange(0,len(clusters_control)):
axs_split[2][l].imshow(cluster_centre[...,2].astype(np.uint8),cmap='gray')
axs_split[2][l].axis('off')
plt.figure(fig_split.number),
plt.savefig(dir_probs + 'clusterCentres_control_splitContrastEnhanced_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
plt.savefig(dir_probs + 'clustercenters_control_splitContrastEnhanced_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
axs[l].imshow(cluster_centre.astype(np.uint8))
axs[l].axis('off')
axs[l].set_title(clusters_control[l])
plt.figure(fig.number),
plt.savefig(dir_probs + 'clusterCentres_control_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
plt.savefig(dir_probs + 'clustercenters_control_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
#sick clusters and contrast enhanced
cluster_centres_sick = kmeans.cluster_centers_[clusters_sick]
clusters_sick = clusters_sick_importance_sorted_noBackground[1:10]
cluster_centers_sick = kmeans.cluster_centers_[clusters_sick]
fig, axs = plt.subplots(1,len(clusters_sick), figsize=(len(clusters_sick)*3,3), sharex=True, sharey=True)
fig.suptitle('Cluster centres for sick')
fig.suptitle('Cluster centers for sick')
if colour_mode!='bnw':
fig_split, axs_split = plt.subplots(3,len(clusters_sick), figsize=(len(clusters_sick)*3,9), sharex=True, sharey=True)
fig_split.suptitle('sick centres split channels (contrast enhanced)')
fig_split.suptitle('sick centers split channels (contrast enhanced)')
for l in np.arange(0,len(clusters_sick)):
if colour_mode == 'bnw':
cluster_centre = np.reshape(cluster_centres_sick[l,:],(patch_size,patch_size))
cluster_centre = np.reshape(cluster_centers_sick[l,:],(patch_size,patch_size))
axs[l].imshow(cluster_centre.astype(np.uint8),cmap='gray')
axs[l].axis('off')
axs[l].set_title(clusters_sick[l])
else:
cluster_centre = np.transpose((np.reshape(cluster_centres_sick[l,:],(3,patch_size,patch_size))),(1,2,0))
cluster_centre = np.transpose((np.reshape(cluster_centers_sick[l,:],(3,patch_size,patch_size))),(1,2,0))
axs_split[0][l].imshow(cluster_centre[...,0].astype(np.uint8),cmap='gray')
axs_split[0][l].axis('off')
axs_split[0][l].set_title(clusters_sick[l])
......@@ -360,24 +426,24 @@ for l in np.arange(0,len(clusters_sick)):
axs_split[2][l].imshow(cluster_centre[...,2].astype(np.uint8),cmap='gray')
axs_split[2][l].axis('off')
plt.figure(fig_split.number),
plt.savefig(dir_probs + 'clusterCentres_'+disease+'_splitContrastEnhanced_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
plt.savefig(dir_probs + 'clustercenters_'+disease+'_splitContrastEnhanced_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
axs[l].imshow(cluster_centre.astype(np.uint8))
axs[l].axis('off')
axs[l].set_title(clusters_sick[l])
plt.figure(fig.number),
plt.savefig(dir_probs + 'clusterCentres_'+disease+'_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
plt.savefig(dir_probs + 'clustercenters_'+disease+'_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
if colour_mode!='bnw':
#plot sick and control clusters with the same intensity range
max_value = []
min_value = []
for l in np.arange(0,len(clusters_control)):
cluster_centre = np.transpose((np.reshape(cluster_centres_control[l,:],(3,patch_size,patch_size))),(1,2,0))
cluster_centre = np.transpose((np.reshape(cluster_centers_control[l,:],(3,patch_size,patch_size))),(1,2,0))
max_value += [cluster_centre.max()]
min_value += [cluster_centre.min()]
for l in np.arange(0,len(clusters_sick)):
cluster_centre = np.transpose((np.reshape(cluster_centres_sick[l,:],(3,patch_size,patch_size))),(1,2,0))
cluster_centre = np.transpose((np.reshape(cluster_centers_sick[l,:],(3,patch_size,patch_size))),(1,2,0))
max_value += [cluster_centre.max()]
min_value += [cluster_centre.min()]
......@@ -385,9 +451,9 @@ if colour_mode!='bnw':
range_min = min(min_value)
fig_split, axs_split = plt.subplots(3,len(clusters_control), figsize=(len(clusters_control)*3,9),sharex=True, sharey=True)
fig_split.suptitle('Control centres split channels (fixed intensity range)')
fig_split.suptitle('Control centers split channels (fixed intensity range)')
for l in np.arange(0,len(clusters_control)):
cluster_centre = np.transpose((np.reshape(cluster_centres_control[l,:],(3,patch_size,patch_size))),(1,2,0))
cluster_centre = np.transpose((np.reshape(cluster_centers_control[l,:],(3,patch_size,patch_size))),(1,2,0))
im = np.zeros(cluster_centre.shape)
im[...,0] = cluster_centre[...,0]
axs_split[0][l].imshow(im.astype(np.uint8))
......@@ -401,13 +467,13 @@ if colour_mode!='bnw':
im[...,2] = cluster_centre[...,2]
axs_split[2][l].imshow(im.astype(np.uint8))
axs_split[2][l].axis('off')
plt.savefig(dir_probs + 'clusterCentres_control_split_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
plt.savefig(dir_probs + 'clustercenters_control_split_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
fig_split, axs_split = plt.subplots(3,len(clusters_sick), figsize=(len(clusters_sick)*3,9),sharex=True, sharey=True)
fig_split.suptitle('sick centres split channels (fixed intensity range)')
fig_split.suptitle('sick centers split channels (fixed intensity range)')
for l in np.arange(0,len(clusters_sick)):
cluster_centre = np.transpose((np.reshape(cluster_centres_sick[l,:],(3,patch_size,patch_size))),(1,2,0))
cluster_centre = np.transpose((np.reshape(cluster_centers_sick[l,:],(3,patch_size,patch_size))),(1,2,0))
im = np.zeros(cluster_centre.shape)
im[...,0] = cluster_centre[...,0]
axs_split[0][l].imshow(im.astype(np.uint8),vmin = range_min, vmax = range_max)
......@@ -421,7 +487,7 @@ if colour_mode!='bnw':
im[...,2] = cluster_centre[...,2]
axs_split[2][l].imshow(im.astype(np.uint8),vmin = range_min, vmax = range_max)
axs_split[2][l].axis('off')
plt.savefig(dir_probs + 'clusterCentres_'+disease+'_split_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
plt.savefig(dir_probs + 'clustercenters_'+disease+'_split_%dclusters_%ddownscale_%dpatchsize.png'%(nr_clusters,1/sc_fac,patch_size), dpi=300)
......
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