def easy_hard_data_classifier_test():
#-------------------------------------------------------------------#
# generate and classify (using nn_classifier) 2 pairs of datasets (easy and hard)
# calculate classification error in each case
trainX, trainY, testX, testY = generate_train_test(2, task='easy')
util.scatter_data(trainX, trainY)
D = scipy.spatial.distance.cdist(testX, trainX, metric='euclidean')
min_index = np.argmin(D, axis=1)
predicted_labels = trainY[min_index]
err = util.classification_error(testY, predicted_labels)
print('True labels:\n{}'.format(testY))
print('Predicted labels:\n{}'.format(predicted_labels))
print('Error:\n{}'.format(err))
trainX, trainY, testX, testY = generate_train_test(2, task='hard')
util.scatter_data(trainX, trainY)
D = scipy.spatial.distance.cdist(testX, trainX, metric='euclidean')
min_index = np.argmin(D, axis=1)
predicted_labels = trainY[min_index]
err = util.classification_error(testY, predicted_labels)
print('True labels:\n{}'.format(testY))
print('Predicted labels:\n{}'.format(predicted_labels))
print('Error:\n{}'.format(err))
#-------------------------------------------------------------------#
pass
def segmentation_combined_atlas_minmax_test():
task = 'brain'
n = 5
all_subjects = np.arange(n)
train_slice = 1
tmp_data, tmp_labels, tmp_feature_labels = util.create_dataset(1,train_slice,task)
all_data_matrix = np.empty((tmp_data.shape[0], tmp_data.shape[1], n))
all_labels_matrix = np.empty((tmp_labels.shape[0], n))
#Load datasets once
for i in all_subjects:
train_data, train_labels, train_feature_labels = util.create_dataset(i+1,train_slice,task)
all_data_matrix[:,:,i] = train_data
all_labels_matrix[:,i] = train_labels.ravel()
predicted_labels_min = seg.segmentation_combined_atlas(all_labels_matrix, combining='min')
predicted_labels_max = seg.segmentation_combined_atlas(all_labels_matrix, combining='max')
test_labels = all_labels_matrix[:,4].astype(bool)
print('Combining method = min:')
err = util.classification_error(test_labels, predicted_labels_min)
print('Error:\n{}'.format(err))
dice = util.dice_overlap(test_labels, predicted_labels_min)
print('Dice coefficient:\n{}'.format(dice))
print('Combining method = max:')
err = util.classification_error(test_labels, predicted_labels_max)
print('Error:\n{}'.format(err))
dice = util.dice_overlap(test_labels, predicted_labels_max)
print('Dice coefficient:\n{}'.format(dice))
def feature_curve(use_random=False):
# Load training and test data
train_data, train_labels, train_feature_labels = util.create_dataset(1, 1, 'brain')
test_data, test_labels, test_feature_labels = util.create_dataset(2, 1, 'brain')
if use_random:
train_data = np.random.randn(train_data.shape[0], train_data.shape[1])
# Normalize data
train_data, test_data = seg.normalize_data(train_data, test_data)
# Define parameters
feature_sizes = np.arange(train_data.shape[1]) + 1
train_size = 10
k = 3
num_iter = 5
# Store errors
test_error = np.empty([len(feature_sizes), num_iter])
test_error[:] = np.nan
train_error = np.empty([len(feature_sizes), num_iter])
train_error[:] = np.nan
# Train and test with different sizes
for i in np.arange(len(feature_sizes)):
for j in np.arange(num_iter):
print('feature size = {}, iter = {}'.format(feature_sizes[i], j))
start_time = timeit.default_timer()
# Subsample training set
ix = np.random.randint(len(train_data), size=train_size)
subset_train_data = train_data[ix, :]
subset_train_labels = train_labels[ix, :]
# Train classifier
neigh = KNeighborsClassifier(n_neighbors=k)
neigh.fit(subset_train_data[:, :feature_sizes[i]], subset_train_labels.ravel())
# Evaluate
predicted_test_labels = neigh.predict(test_data[:, :feature_sizes[i]])
predicted_train_labels = neigh.predict(subset_train_data[:, :feature_sizes[i]])
test_error[i, j] = util.classification_error(test_labels, predicted_test_labels)
train_error[i, j] = util.classification_error(subset_train_labels, predicted_train_labels)
# Timer log
elapsed = timeit.default_timer() - start_time
# print('elapsed time = {}'.format(elapsed))
## Display results
fig = plt.figure(figsize=(8, 8))
ax1 = fig.add_subplot(111)
x = feature_sizes
y_test = np.mean(test_error, 1)
yerr_test = np.std(test_error, 1)
p1 = ax1.errorbar(x, y_test, yerr=yerr_test, label='Test error')
ax1.set_xlabel('Number of features')
ax1.set_ylabel('Error')
ax1.grid()
ax1.legend()
def easy_hard_data_classifier_test():
train_data_easy, train_label_easy, test_data_easy, test_label_easy = generate_train_test(50, 'easy')
train_data_hard, train_label_hard, test_data_hard, test_label_hard = generate_train_test(50, 'hard')
predicted_labels_easy = seg.nn_classifier(train_data_easy, train_label_easy, test_data_easy)
predicted_labels_hard = seg.nn_classifier(train_data_hard, train_label_hard, test_data_hard)
err_easy = util.classification_error(test_label_easy, predicted_labels_easy)
err_hard = util.classification_error(test_label_hard, predicted_labels_hard)
print("easy: {0}, hard: {1}".format(err_easy, err_hard))
def TestCombinedKmeans(train_data_matrix, train_labels_matrix, test_data,
test_labels):
im_size = [240, 240]
#Combined K-means
pred_labels_kmeans = np.empty(
[train_data_matrix.shape[0], train_data_matrix.shape[2]])
for i in range(train_data_matrix.shape[2]):
#normalize data
train_data, test_data = seg.normalize_data(train_data_matrix[:, :, i],
test_data)
#get optimized clusters (using 100 iterations and a learning rate of 0.1)
_, _, w_final = prj.kmeans_no_plot(train_data,
train_labels_matrix[:, i], 100, 0.1)
#predict the labels
temp_pred = prj.predicted_kmeans_test(w_final, test_data)
#store labels for each training subject in one matrix
pred_labels_kmeans[:, i] = temp_pred
#decision fusion based on majority voting
predicted_labels_kmeans_final = scipy.stats.mode(pred_labels_kmeans,
axis=1)[0].flatten()
#calculate the error and dice
err = util.classification_error(test_labels, predicted_labels_kmeans_final)
dice = util.dice_multiclass(test_labels, predicted_labels_kmeans_final)
predicted_mask = predicted_labels_kmeans_final.reshape(
im_size[0], im_size[1])
return predicted_mask, err, dice
def segmentation_combined_atlas_test():
task = 'brain'
n = 5
all_subjects = np.arange(n)
train_slice = 1
tmp_data, tmp_labels, tmp_feature_labels = util.create_dataset(1,train_slice,task)
all_data_matrix = np.empty((tmp_data.shape[0], tmp_data.shape[1], n))
all_labels_matrix = np.empty((tmp_labels.shape[0], n))
#Load datasets once
for i in all_subjects:
train_data, train_labels, train_feature_labels = util.create_dataset(i+1,train_slice,task)
all_data_matrix[:,:,i] = train_data
all_labels_matrix[:,i] = train_labels.ravel()
#------------------------------------------------------------------#
# TODO: Use provided code to Combine labels of training images,
# Convert combined label into mask image,
# Convert true label into mask image, and
# View both masks on the same axis,
# Also calculate dice coefficient and error
prlabes=seg.segmentation_combined_atlas(all_labels_matrix)
predicted_mask= prlabes.reshape(240,240)
GT = plt.imread('../data/dataset_brains/1_1_gt.tif')
gt_mask = GT > 0
gt_vec = gt_mask.flatten() # labels
MSE= util.classification_error(gt_vec,prlabes)
Dice=util.dice_overlap(gt_vec,prlabes)
print('MSE',MSE,'Dice',Dice)
plt.figure()
plt.imshow(predicted_mask, cmap='gray', alpha=0.5)
plt.imshow(gt_mask, cmap='jet', alpha=0.5)
plt.show()
def segmentation_combined_atlas_test():
task = 'brain'
n = 5
all_subjects = np.arange(n)
train_slice = 1
tmp_data, tmp_labels, tmp_feature_labels = util.create_dataset(1, train_slice, task)
all_data_matrix = np.empty((tmp_data.shape[0], tmp_data.shape[1], n))
all_labels_matrix = np.empty((tmp_labels.shape[0], n))
# Load datasets once
for i in all_subjects:
train_data, train_labels, train_feature_labels = util.create_dataset(i + 1, train_slice, task)
all_data_matrix[:, :, i] = train_data
all_labels_matrix[:, i] = train_labels.ravel()
# Combine labels of training images:
predicted_labels = seg.segmentation_combined_atlas(all_labels_matrix, combining='mode')
# Convert combined label into mask image:
predicted_mask = predicted_labels.reshape(240, 240)
# Convert true label into mask image:
true_mask = all_labels_matrix[:, 4].reshape(240, 240)
plt.imshow(predicted_mask + true_mask)
err = util.classification_error(true_mask, predicted_mask)
dice = util.dice_overlap(true_mask, predicted_mask)
print("error: {0}, dice: {1}".format(err, dice))
def nn_classifier_test_brains(testDice=False):
# Subject 1, slice 1 is the train data
X, Y, feature_labels_train = util.create_dataset(1,1,'brain')
N = 1000
ix = np.random.randint(len(X), size=N)
train_data = X[ix,:]
train_labels = Y[ix,:]
# Subject 3, slice 1 is the test data
test_data, test_labels, feature_labels_test = util.create_dataset(3,1,'brain')
predicted_labels = seg.nn_classifier(train_data, train_labels, test_data)
predicted_labels = predicted_labels.astype(bool)
test_labels = test_labels.astype(bool)
err = util.classification_error(test_labels, predicted_labels)
print('Error:\n{}'.format(err))
if testDice:
dice = util.dice_overlap(test_labels, predicted_labels)
print('Dice coefficient:\n{}'.format(dice))
else:
I = plt.imread('../data/dataset_brains/3_1_t1.tif')
GT = plt.imread('../data/dataset_brains/3_1_gt.tif')
gt_mask = GT>0
gt_labels = gt_mask.flatten() # labels
predicted_mask = predicted_labels.reshape(I.shape)
fig = plt.figure(figsize=(15,5))
ax1 = fig.add_subplot(131)
ax1.imshow(I)
ax2 = fig.add_subplot(132)
ax2.imshow(predicted_mask)
ax3 = fig.add_subplot(133)
ax3.imshow(gt_mask)
def easy_hard_data_classifier_test():
#-------------------------------------------------------------------#
#TODO: generate and classify (using nn_classifier) 2 pairs of datasets (easy and hard)
# calculate classification error in each case
EasytrainX, EasytrainY, EasytestX, EasytestY = generate_train_test(100, 'easy')
HardtrainX, HardtrainY, HardtestX, HardtestY = generate_train_test(100, 'hard')
predictedLabels_easy = seg.nn_classifier(EasytrainX, EasytrainY, EasytestX)
class_error_easy = util.classification_error(EasytrainY, predictedLabels_easy)
predictedLabels_hard = seg.nn_classifier(EasytrainX, EasytrainY, EasytestX)
class_error_hard = util.classification_error(HardtrainX, HardtrainY, predictedLabels_hard)
print("Easy task's error: {}".format(class_error_easy))
print("Hard task's error: {}".format(class_error_hard))
#-------------------------------------------------------------------#
pass
def easy_hard_data_classifier_test():
#-------------------------------------------------------------------#
#TODO: generate and classify (using nn_classifier) 2 pairs of datasets (easy and hard)
# calculate classification error in each case
trainXeasy, trainYeasy, testXeasy, testYeasy = generate_train_test(
2, 'easy')
trainXhard, trainYhard, testXhard, testYhard = generate_train_test(
2, 'hard')
predicted_labels_easy = seg.nn_classifier(trainXeasy, trainYeasy,
testXeasy)
predicted_labels_hard = seg.nn_classifier(trainXhard, trainYhard,
testXhard)
err_easy = util.classification_error(testYeasy, predicted_labels_easy)
print(err_easy)
err_hard = util.classification_error(testYhard, predicted_labels_hard)
print(err_hard)
def knn_curve():
# Load training and test data
train_data, train_labels, train_feature_labels = util.create_dataset(
1, 1, 'brain')
test_data, test_labels, test_feature_labels = util.create_dataset(
2, 1, 'brain')
# Normalize data
train_data, test_data = seg.normalize_data(train_data, test_data)
#Define parameters
num_iter = 3
train_size = 100
k = np.array([1, 3, 5, 9, 15, 25, 100])
# k = np.array([1, 5, 9])
#Store errors
test_error = np.empty([len(k), num_iter])
test_error[:] = np.nan
dice = np.empty([len(k), num_iter])
dice[:] = np.nan
## Train and test with different values
for i in np.arange(len(k)):
for j in np.arange(num_iter):
print('k = {}, iter = {}'.format(k[i], j))
#Subsample training set
ix = np.random.randint(len(train_data), size=train_size)
subset_train_data = train_data[ix, :]
subset_train_labels = train_labels[ix, :]
predicted_test_labels = seg.knn_classifier(subset_train_data,
subset_train_labels,
test_data, k[i])
# #Train classifier
# neigh = KNeighborsClassifier(n_neighbors=k[i])
# neigh.fit(subset_train_data, subset_train_labels)
# #Evaluate
# predicted_test_labels = neigh.predict(test_data)
test_error[i,
j] = util.classification_error(test_labels,
predicted_test_labels)
dice[i, j] = util.dice_overlap(test_labels, predicted_test_labels)
## Display results
fig = plt.figure(figsize=(8, 8))
ax1 = fig.add_subplot(111)
p1 = ax1.plot(k, np.mean(test_error, 1), 'r', label='error')
p2 = ax1.plot(k, np.mean(dice, 1), 'k', label='dice')
ax1.set_xlabel('k')
ax1.set_ylabel('error')
ax1.grid()
ax1.legend()
def TestcombinedAtlases(train_labels_matrix, test_labels):
im_size = [240, 240]
#predict the test data labels
predicted_labels, predicted_labels2_atlas = seg.segmentation_combined_atlas(
train_labels_matrix)
#calculate error and dice
dice_atlas = util.dice_multiclass(test_labels, predicted_labels2_atlas)
err_atlas = util.classification_error(test_labels, predicted_labels2_atlas)
predicted_mask_atlas = predicted_labels2_atlas.reshape(
im_size[0], im_size[1])
return predicted_mask_atlas, err_atlas, dice_atlas,
def nn_classifier_test_samples():
train_data, train_labels = seg.generate_gaussian_data(20)
test_data, test_labels = seg.generate_gaussian_data(10)
predicted_labels = seg.nn_classifier(train_data, train_labels, test_data)
# predicted_labels = predicted_labels.astype(bool)
# test_labels = test_labels.astype(bool)
err = util.classification_error(test_labels, predicted_labels)
print('True labels:\n{}'.format(test_labels))
print('Predicted labels:\n{}'.format(predicted_labels))
print('Error:\n{}'.format(err))
def TestcombinedkNN(train_data_matrix, train_labels_matrix, test_data,
test_labels):
im_size = [240, 240]
#predict test data labels
predicted_labels, predicted_labels2_knn = seg.segmentation_combined_knn(
train_data_matrix, train_labels_matrix, test_data)
#calculate error and dice
dice_knn = util.dice_multiclass(test_labels, predicted_labels2_knn)
err_knn = util.classification_error(test_labels, predicted_labels2_knn)
predicted_mask_atlas = predicted_labels2_knn.reshape(
im_size[0], im_size[1])
return predicted_mask_atlas, err_knn, dice_knn
def segmentation_combined_atlas_test():
task = 'brain'
n = 5
all_subjects = np.arange(n)
train_slice = 1
tmp_data, tmp_labels, tmp_feature_labels = util.create_dataset(1,train_slice,task)
all_data_matrix = np.empty((tmp_data.shape[0], tmp_data.shape[1], n))
all_labels_matrix = np.empty((tmp_labels.shape[0], n))
#Load datasets once
for i in all_subjects:
train_data, train_labels, train_feature_labels = util.create_dataset(i+1,train_slice,task)
all_data_matrix[:,:,i] = train_data
all_labels_matrix[:,i] = train_labels.ravel()
#------------------------------------------------------------------#
# TODO: Use provided code to Combine labels of training images,
# Convert combined label into mask image,
# Convert true label into mask image, and
# View both masks on the same axis,
# Also calculate dice coefficient and error
# Combine labels of training images:
predicted_labels = stats.mode(all_labels_matrix[:,:4], axis=1)[0]
# Convert combined label into mask image:
predicted_mask = predicted_labels.reshape(240,240)
# Convert true label into mask image:
true_mask = all_labels_matrix[:,4].reshape(240,240)
# View both masks on the same axis using imshow()
class_error = util.classification_error(all_labels_matrix[:,4], predicted_labels)
dice_Overlap = util.dice_overlap(all_labels_matrix[:,4], predicted_labels)
print("The error: {:.2f}".format(class_error))
print("Dice coefficient: {:.2f}".format(dice_Overlap))
fig = plt.figure(figsize=(10,13))
ax1 = fig.add_subplot(121)
ax1.imshow(predicted_mask, cmap = 'Oranges_r')
ax2 = fig.add_subplot(122)
ax2.imshow(true_mask)
def segmentation_mymethod(train_data,
train_labels,
test_data,
test_labels,
task='brain',
method='nearest neighbour',
testDice=True):
# segments the image based on your own method!
# Input:
# train_data_matrix num_pixels x num_features x num_subjects matrix of
# features
# train_labels_matrix num_pixels x num_subjects matrix of labels
# test_data num_pixels x num_features test data
# task String corresponding to the segmentation task: either 'brain' or 'tissue'
# Output:
# predicted_labels Predicted labels for the test slice
#------------------------------------------------------------------#
#TODO: Implement your method here
if method == 'kmeans':
predicted_labels = seg.kmeans_clustering(test_data, K=4)
elif method == 'nearest neighbour':
predicted_labels = seg.nn_classifier(train_data, train_labels,
test_data)
elif method == 'knn':
predicted_labels = seg.knn_classifier(train_data,
train_labels,
test_data,
k=4)
elif method == 'atlas':
predicted_labels = seg.segmentation_atlas(train_data, train_labels,
test_data)
predicted_labels = predicted_labels.astype(bool)
test_labels = test_labels.astype(bool)
err = util.classification_error(test_labels, predicted_labels)
print('Error:\n{}'.format(err))
if testDice:
dice = util.dice_multiclass(test_labels, predicted_labels)
print('Dice coefficient:\n{}'.format(dice))
#------------------------------------------------------------------#
return predicted_labels
def segmentation_demo():
#only SECTION 2 is needed for what we want to do
train_subject = 1
test_subject = 2
train_slice = 1
test_slice = 1
task = 'tissue'
#SECTION 1 (this seciton has nothing to do with SECTION 2)
#Load data from a train and testsubject
train_data, train_labels, train_feature_labels = util.create_dataset(
train_subject, train_slice, task)
test_data, test_labels, test_feature_labels = util.create_dataset(
test_subject, test_slice, task)
util.scatter_data(train_data, train_labels, 0, 6)
util.scatter_data(test_data, test_labels, 0, 6)
predicted_labels = seg.segmentation_atlas(None, train_labels, None)
err = util.classification_error(test_labels, predicted_labels)
dice = util.dice_overlap(test_labels, predicted_labels)
#Display results
true_mask = test_labels.reshape(240, 240)
predicted_mask = predicted_labels.reshape(240, 240)
fig = plt.figure(figsize=(8, 8))
ax1 = fig.add_subplot(111)
ax1.imshow(true_mask, 'gray')
ax1.imshow(predicted_mask, 'viridis', alpha=0.5)
print('Subject {}, slice {}.\nErr {}, dice {}'.format(
test_subject, test_slice, err, dice))
## SECTION 2:Compare methods
num_images = 5
num_methods = 3
im_size = [240, 240]
all_errors = np.empty([num_images, num_methods])
all_errors[:] = np.nan
all_dice = np.empty([num_images, num_methods])
all_dice[:] = np.nan
all_subjects = np.arange(5) #list of all subjects [0, 1, 2, 3, 4]
train_slice = 2
task = 'tissue'
all_data_matrix = np.empty(
[train_data.shape[0], train_data.shape[1], num_images])
all_labels_matrix = np.empty([train_labels.size, num_images])
#Load datasets once
print('Loading data for ' + str(num_images) + ' subjects...')
for i in all_subjects:
sub = i + 1
train_data, train_labels, train_feature_labels = util.create_dataset(
sub, train_slice, task)
all_data_matrix[:, :, i] = train_data
all_labels_matrix[:, i] = train_labels.flatten()
print('Finished loading data.\nStarting segmentation...')
#Go through each subject, taking i-th subject as the test
for i in all_subjects:
sub = i + 1
#Define training subjects as all, except the test subject
train_subjects = all_subjects.copy()
train_subjects = np.delete(train_subjects, i)
train_data_matrix = all_data_matrix[:, :, train_subjects]
train_labels_matrix = all_labels_matrix[:, train_subjects]
test_data = all_data_matrix[:, :, i]
test_labels = all_labels_matrix[:, i]
test_shape_1 = test_labels.reshape(im_size[0], im_size[1])
fig = plt.figure(figsize=(15, 5))
predicted_labels, predicted_labels2 = seg.segmentation_combined_atlas(
train_labels_matrix)
all_errors[i, 0] = util.classification_error(test_labels,
predicted_labels2)
all_dice[i, 0] = util.dice_multiclass(test_labels, predicted_labels2)
predicted_mask_1 = predicted_labels2.reshape(im_size[0], im_size[1])
ax1 = fig.add_subplot(131)
ax1.imshow(test_shape_1, 'gray')
ax1.imshow(predicted_mask_1, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 0],
all_dice[i, 0])
ax1.set_xlabel(text_str)
ax1.set_title('Subject {}: Combined atlas'.format(sub))
predicted_labels, predicted_labels2 = seg.segmentation_combined_knn(
train_data_matrix, train_labels_matrix, test_data)
all_errors[i, 1] = util.classification_error(test_labels,
predicted_labels2)
all_dice[i, 1] = util.dice_multiclass(test_labels, predicted_labels2)
predicted_mask_2 = predicted_labels2.reshape(im_size[0], im_size[1])
ax2 = fig.add_subplot(132)
ax2.imshow(test_shape_1, 'gray')
ax2.imshow(predicted_mask_2, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 1],
all_dice[i, 1])
ax2.set_xlabel(text_str)
ax2.set_title('Subject {}: Combined k-NN'.format(sub))
#OUR METHOD
#predict the labels using our method
predicted_labels_mymethod = segmentation_mymethod(train_data_matrix,
train_labels_matrix,
test_data,
num_iter=100,
mu=0.1)
#determine error and dice (multiclass, since there are more classes)
all_errors[i,
2] = util.classification_error(test_labels,
predicted_labels_mymethod)
all_dice[i, 2] = util.dice_multiclass(test_labels,
predicted_labels_mymethod)
#reshape the predicted labels in order to plot the results
predicted_mask_3 = predicted_labels_mymethod.reshape(
im_size[0], im_size[1])
#plot the predicted image over the real image
plt.imshow(predicted_mask_3, 'viridis')
ax3 = fig.add_subplot(133)
ax3.imshow(test_shape_1, 'gray')
ax3.imshow(predicted_mask_3, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 2],
all_dice[i, 2])
ax3.set_xlabel(text_str)
ax3.set_title('Subject {}: My method'.format(sub))
#save the figure after every loop (3 subimages/plots)
fig.savefig("Results for test subject {}".format(sub), )
def learning_curve():
# Load training and test data
train_data, train_labels = seg.generate_gaussian_data(1000)
test_data, test_labels = seg.generate_gaussian_data(1000)
[train_data, test_data] = seg.normalize_data(train_data, test_data)
#Define parameters
train_sizes = np.array([1, 3, 10, 30, 100, 300])
k = 1
num_iter = 3 #How often to repeat the experiment
#Store errors
test_error = np.empty([len(train_sizes),num_iter])
test_error[:] = np.nan
test_dice = np.empty([len(train_sizes),num_iter])
test_dice[:] = np.nan
#------------------------------------------------------------------#
#TODO: Store errors for training data
#------------------------------------------------------------------#
## Train and test with different values
for i in np.arange(len(train_sizes)):
for j in np.arange(num_iter):
print('train_size = {}, iter = {}'.format(train_sizes[i], j))
#Subsample training set
ix = np.random.randint(len(train_data), size=train_sizes[i])
subset_train_data = train_data[ix,:]
subset_train_labels = train_labels[ix,:]
#Train classifier
neigh = KNeighborsClassifier(n_neighbors=k)
neigh.fit(subset_train_data, subset_train_labels.ravel())
#Evaluate
predicted_test_labels = neigh.predict(test_data)
test_labels = test_labels.astype(bool)
predicted_test_labels = predicted_test_labels.astype(bool)
test_error[i,j] = util.classification_error(test_labels, predicted_test_labels)
test_dice[i,j] = util.dice_overlap(test_labels, predicted_test_labels)
#------------------------------------------------------------------#
#TODO: Predict training labels and evaluate
#------------------------------------------------------------------#
## Display results
fig = plt.figure(figsize=(8,8))
ax1 = fig.add_subplot(111)
x = np.log(train_sizes)
y_test = np.mean(test_error,1)
yerr_test = np.std(test_error,1)
p1 = ax1.errorbar(x, y_test, yerr=yerr_test, label='Test error')
#------------------------------------------------------------------#
#TODO: Plot training size
#------------------------------------------------------------------#
ax1.set_xlabel('Number of training samples (k)')
ax1.set_ylabel('error')
ticks = list(x)
ax1.set_xticks(ticks)
tick_lbls = [str(i) for i in train_sizes]
ax1.set_xticklabels(tick_lbls)
ax1.grid()
ax1.legend()
def learning_curve():
# Load training and test data
# train_data, train_labels = seg.generate_gaussian_data(1000)
train_data, train_labels, _ = util.create_dataset(1, 1, 'brain')
# test_data, test_labels = seg.generate_gaussian_data(1000)
test_data, test_labels, _ = util.create_dataset(2, 1, 'brain')
train_data, test_data = seg.normalize_data(train_data, test_data)
# Define parameters
train_sizes = np.logspace(0.1, 3.0, num=15).astype(int)
k = 1
num_iter = 3 # How often to repeat the experiment
# Store errors
test_error = np.empty([len(train_sizes), num_iter])
test_error[:] = np.nan
test_dice = np.empty([len(train_sizes), num_iter])
test_dice[:] = np.nan
train_error = np.empty([len(train_sizes), num_iter])
train_error[:] = np.nan
train_dice = np.empty([len(train_sizes), num_iter])
train_dice[:] = np.nan
## Train and test with different values
for i in np.arange(len(train_sizes)):
for j in np.arange(num_iter):
print('train_size = {}, iter = {}'.format(train_sizes[i], j))
# Subsample training set
ix = np.random.randint(len(train_data), size=train_sizes[i])
subset_train_data = train_data[ix, :]
subset_train_labels = train_labels[ix, :]
# Train classifier
neigh = KNeighborsClassifier(n_neighbors=k)
neigh.fit(subset_train_data, subset_train_labels.ravel())
# Evaluate
predicted_test_labels = neigh.predict(test_data)
test_labels = test_labels.astype(bool)
predicted_test_labels = predicted_test_labels.astype(bool)
test_error[i, j] = util.classification_error(test_labels, predicted_test_labels)
test_dice[i, j] = util.dice_overlap(test_labels, predicted_test_labels)
predicted_train_labels = neigh.predict(train_data).astype(bool)
train_labels_bool = train_labels.astype(bool)
train_error[i, j] = util.classification_error(train_labels_bool, predicted_train_labels)
train_dice[i, j] = util.dice_overlap(train_labels_bool, predicted_train_labels)
## Display results
fig = plt.figure(figsize=(8, 8))
gs = fig.add_gridspec(2, 2)
ax1 = fig.add_subplot(gs[0, :])
ax2 = fig.add_subplot(gs[1, :])
x = np.log(train_sizes)
ticks = list(x)
tick_lbls = [str(i) for i in train_sizes]
y_test = np.mean(test_error, 1)
y_train = np.mean(train_error, 1)
yerr_test = np.std(test_error, 1)
yerr_train = np.std(train_error, 1)
p1 = ax1.errorbar(x, y_test, yerr=yerr_test, label='Test error')
p2 = ax2.errorbar(x, y_train, yerr=yerr_train, label='Train error')
ax1.set_xlabel('Number of training samples (k)')
ax1.set_ylabel('error')
ax1.set_xticks(ticks)
ax1.set_xticklabels(tick_lbls)
ax1.grid()
ax1.legend()
ax2.set_xlabel('Number of training samples (k)')
ax2.set_ylabel('error')
ax2.set_xticks(ticks)
ax2.set_xticklabels(tick_lbls)
ax2.grid()
ax2.legend()
def segmentation_demo():
# Data name specification
train_subject = 1
test_subject = 2
train_slice = 1
test_slice = 1
task = 'tissue'
# Load data
train_data, train_labels, train_feature_labels = util.create_dataset(
train_subject, train_slice, task)
test_data, test_labels, test_feature_labels = util.create_dataset(
test_subject, test_slice, task)
# find the predicted labels (here: the train_labels)
predicted_labels = seg.segmentation_atlas(None, train_labels, None)
# Calculate the error and dice score of these predicted labels in comparison to test labels
err = util.classification_error(test_labels, predicted_labels)
dice = util.dice_overlap(test_labels, predicted_labels)
# Display results
true_mask = test_labels.reshape(240, 240)
predicted_mask = predicted_labels.reshape(240, 240)
fig = plt.figure(figsize=(8, 8))
ax1 = fig.add_subplot(111)
ax1.imshow(true_mask, 'gray')
ax1.imshow(predicted_mask, 'viridis', alpha=0.5)
print('Subject {}, slice {}.\nErr {}, dice {}'.format(
test_subject, test_slice, err, dice))
# COMPARE METHODS
num_images = 5
num_methods = 3
im_size = [240, 240]
# make space for error and dice data
all_errors = np.empty([num_images, num_methods])
all_errors[:] = np.nan
all_dice = np.empty([num_images, num_methods])
all_dice[:] = np.nan
# data name specification
all_subjects = np.arange(num_images)
train_slice = 1
task = 'tissue'
# make space for data
all_data_matrix = np.empty(
[train_data.shape[0], train_data.shape[1], num_images])
# all_labels_matrix = np.empty([train_labels.size, num_images], dtype=bool)
all_labels_matrix = np.empty([train_labels.size, num_images])
# Load datasets once
print('Loading data for ' + str(num_images) + ' subjects...')
for i in all_subjects:
sub = i + 1
train_data, train_labels, train_feature_labels = util.create_dataset(
sub, train_slice, task)
all_data_matrix[:, :, i] = train_data
all_labels_matrix[:, i] = train_labels.flatten()
print('Finished loading data.\nStarting segmentation...')
# Go through each subject, taking i-th subject as the test
for i in np.arange(num_images):
sub = i + 1
# Define training subjects as all, except the test subject
train_subjects = all_subjects.copy()
train_subjects = np.delete(train_subjects, i)
# Obtain data about the chosen amount of subjects
train_data_matrix = all_data_matrix[:, :, train_subjects]
train_labels_matrix = all_labels_matrix[:, train_subjects]
test_data = all_data_matrix[:, :, i]
test_labels = all_labels_matrix[:, i]
test_shape_1 = test_labels.reshape(im_size[0], im_size[1])
fig = plt.figure(figsize=(15, 5))
# Get predicted labels from atlas method
predicted_labels = seg.segmentation_combined_atlas(train_labels_matrix)
all_errors[i, 0] = util.classification_error(test_labels,
predicted_labels)
all_dice[i, 0] = util.dice_overlap(test_labels, predicted_labels)
# Plot atlas method
predicted_mask_1 = predicted_labels.reshape(im_size[0], im_size[1])
ax1 = fig.add_subplot(131)
ax1.imshow(test_shape_1, 'gray')
ax1.imshow(predicted_mask_1, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 0],
all_dice[i, 0])
ax1.set_xlabel(text_str)
ax1.set_title('Subject {}: Combined atlas'.format(sub))
# Get predicted labels from kNN method
predicted_labels = seg.segmentation_combined_knn(
train_data_matrix, train_labels_matrix, test_data)
all_errors[i, 1] = util.classification_error(test_labels,
predicted_labels)
all_dice[i, 1] = util.dice_overlap(test_labels, predicted_labels)
# Plot kNN method
predicted_mask_2 = predicted_labels.reshape(im_size[0], im_size[1])
ax2 = fig.add_subplot(132)
ax2.imshow(test_shape_1, 'gray')
ax2.imshow(predicted_mask_2, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 1],
all_dice[i, 1])
ax2.set_xlabel(text_str)
ax2.set_title('Subject {}: Combined k-NN'.format(sub))
# Get predicted labels from my own method
predicted_labels = segmentation_mymethod(train_data_matrix,
train_labels_matrix,
test_data, task)
print(predicted_labels.shape)
print(np.unique(predicted_labels))
all_errors[i, 2] = util.classification_error(test_labels,
predicted_labels)
all_dice[i, 2] = util.dice_overlap(test_labels, predicted_labels)
# Plot my own method
predicted_mask_3 = predicted_labels.reshape(im_size[0], im_size[1])
ax3 = fig.add_subplot(133)
ax3.imshow(test_shape_1, 'gray')
ax3.imshow(predicted_mask_3, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 2],
all_dice[i, 2])
ax3.set_xlabel(text_str)
ax3.set_title('Subject {}: My method'.format(sub))
#Combined K-means
pred_labels_kmeans = np.empty(
[train_data_matrix.shape[0], train_data_matrix.shape[2]])
print(train_data.shape)
for i in range(train_data_matrix.shape[2]):
train_data, test_data = seg.normalize_data(train_data_matrix[:, :, i],
test_data)
_, _, w_final = prj.kmeans(train_data, train_labels_matrix[:, i], num_iter,
mu)
temp_pred = prj.predicted_kmeans_test(w_final, test_data)
print("Possible classes are: {}".format(np.unique(temp_pred)))
tempdice = util.dice_multiclass(test_labels, temp_pred)
temperr = util.classification_error(test_labels, temp_pred)
print('Err {:.4f}, dice {:.4f}'.format(temperr, tempdice))
pred_labels_kmeans[:, i] = temp_pred
#decision fusion
predicted_labels_kmeans_final = scipy.stats.mode(pred_labels_kmeans,
axis=1)[0].flatten()
#do a check which labels exist
print("Possible classes are: {}".format(
np.unique(predicted_labels_kmeans_final)))
#calculate the error and dice
err = util.classification_error(test_labels, predicted_labels_kmeans_final)
dice = util.dice_multiclass(test_labels, predicted_labels_kmeans_final)
def segmentation_demo():
# Data name specification
train_subject = 1
test_subject = 2
train_slice = 1
test_slice = 1
task = 'tissue'
# Load data
train_data, train_labels, train_feature_labels = util.create_dataset(
train_subject, train_slice, task)
test_data, test_labels, test_feature_labels = util.create_dataset(
test_subject, test_slice, task)
# Normalize and feed data through X_pca
train_norm, _ = seg.normalize_data(train_data)
Xpca, v, w, fraction_variance, ix = seg.mypca(train_norm)
relevant_feature = int(np.sum(fraction_variance < 0.95)) + 1
train_norm_ord = train_norm[:, ix]
train_norm = train_norm_ord[:, :relevant_feature]
# find the predicted labels (here: the train_labels)
predicted_labels = seg.segmentation_atlas(None, train_labels, None)
# Calculate the error and dice score of these predicted labels in comparison to test labels
err = util.classification_error(test_labels, predicted_labels)
dice = util.dice_multiclass(test_labels, predicted_labels)
# Display results
true_mask = test_labels.reshape(240, 240)
predicted_mask = predicted_labels.reshape(240, 240)
fig = plt.figure(figsize=(8, 8))
ax1 = fig.add_subplot(111)
ax1.imshow(true_mask, 'gray')
ax1.imshow(predicted_mask, 'viridis', alpha=0.5)
print('Subject {}, slice {}.\nErr {}, dice {}'.format(
test_subject, test_slice, err, dice))
# COMPARE METHODS
num_images = 5
num_methods = 3
im_size = [240, 240]
# make space for error and dice data
all_errors = np.empty([num_images, num_methods])
all_errors[:] = np.nan
all_dice = np.empty([num_images, num_methods])
all_dice[:] = np.nan
# data name specification
all_subjects = np.arange(num_images)
train_slice = 1
task = 'tissue'
# make space for data
all_data_matrix = np.empty(
[train_norm.shape[0], train_norm.shape[1], num_images])
all_labels_matrix = np.empty([train_labels.size, num_images])
all_data_matrix_kmeans = np.empty(
[train_norm.shape[0], train_norm.shape[1], num_images])
all_labels_matrix_kmeans = np.empty([train_labels.size, num_images])
# Load datasets once
print('Loading data for ' + str(num_images) + ' subjects...')
for i in all_subjects:
sub = i + 1
train_data, train_labels, train_feature_labels = util.create_dataset(
sub, train_slice, task)
train_norm, _ = seg.normalize_data(train_data)
Xpca, v, w, fraction_variance, ix = seg.mypca(train_norm)
relevant_labels = int(np.sum(fraction_variance < 0.95)) + 1
train_norm_ord = train_norm[:, ix]
train_norm = train_norm_ord[:, :relevant_labels]
all_data_matrix[:, :, i] = train_norm
all_labels_matrix[:, i] = train_labels.flatten()
# Load datasets for kmeans
print('Loading data for ' + str(num_images) + ' subjects...')
for i in all_subjects:
sub = i + 1
train_data_kmeans, train_labels_kmeans, train_feature_labels_kmeans = create_dataset(
sub, train_slice, task)
train_norm_kmeans, _ = seg.normalize_data(train_data_kmeans)
all_data_matrix_kmeans[:, :, i] = train_norm_kmeans
all_labels_matrix_kmeans[:, i] = train_labels_kmeans.flatten()
print('Finished loading data.\nStarting segmentation...')
# Go through each subject, taking i-th subject as the test
for i in np.arange(num_images):
sub = i + 1
# Define training subjects as all, except the test subject
train_subjects = all_subjects.copy()
train_subjects = np.delete(train_subjects, i)
# Obtain data about the chosen amount of subjects
train_data_matrix = all_data_matrix[:, :, train_subjects]
train_labels_matrix = all_labels_matrix[:, train_subjects]
test_data = all_data_matrix[:, :, i]
test_labels = all_labels_matrix[:, i]
test_shape_1 = test_labels.reshape(im_size[0], im_size[1])
fig = plt.figure(figsize=(15, 5))
# Get predicted labels from atlas method
predicted_labels = seg.segmentation_combined_atlas(train_labels_matrix)
all_errors[i, 0] = util.classification_error(test_labels,
predicted_labels)
all_dice[i, 0] = util.dice_multiclass(test_labels, predicted_labels)
# Plot atlas method
predicted_mask_1 = predicted_labels.reshape(im_size[0], im_size[1])
ax1 = fig.add_subplot(151)
ax1.imshow(test_shape_1, 'gray')
ax1.imshow(predicted_mask_1, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 0],
all_dice[i, 0])
ax1.set_xlabel(text_str)
ax1.set_title('Subject {}: Combined atlas'.format(sub))
# Get predicted labels from kNN method
predicted_labels = seg.segmentation_combined_knn(train_data_matrix,
train_labels_matrix,
test_data,
k=10)
all_errors[i, 1] = util.classification_error(test_labels,
predicted_labels)
all_dice[i, 1] = util.dice_multiclass(test_labels, predicted_labels)
# Plot kNN method
predicted_mask_2 = predicted_labels.reshape(im_size[0], im_size[1])
ax2 = fig.add_subplot(152)
ax2.imshow(test_shape_1, 'gray')
ax2.imshow(predicted_mask_2, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 1],
all_dice[i, 1])
ax2.set_xlabel(text_str)
ax2.set_title('Subject {}: Combined k-NN'.format(sub))
# Get predicted labels from my own method
# all_data_matrix_bnb = np.empty([train_norm.shape[0], train_norm.shape[1], num_images])
# all_labels_matrix_bnb = np.empty([train_labels.size, num_images])
# for ii in all_subjects:
# sub = i + 1
# task = 'brain'
# train_data_bnb, train_labels_bnb, train_feature_labels_bnb = util.create_dataset(sub, train_slice, task)
# train_norm_bnb, _ = seg.normalize_data(train_data_bnb)
# Xpca, v, w, fraction_variance, ix = seg.mypca(train_norm_bnb)
# relevant_labels_bnb = int(np.sum(fraction_variance < 0.95)) + 1
# train_norm_ord_bnb = train_norm_bnb[:, ix]
# train_norm_bnb = train_norm_ord_bnb[:, :relevant_labels_bnb]
# all_data_matrix_bnb[:, :, ii] = train_norm_bnb
# all_labels_matrix_bnb[:, ii] = train_labels_bnb.flatten()
#
# qw, we, er = all_data_matrix.shape
# for iii in np.arange(qw):
# for j in np.arange(er):
# if all_labels_matrix_bnb[iii, j] == 0:
# for k in np.arange(we):
# all_data_matrix[iii, k, j] = 0
# train_data_matrix = all_data_matrix[:, :, train_subjects]
# test_data = all_data_matrix[:, :, i]
train_data_matrix_kmeans = all_data_matrix_kmeans[:, :, train_subjects]
train_labels_matrix_kmeans = all_labels_matrix[:, train_subjects]
test_data_kmeans = all_data_matrix_kmeans[:, :, i]
predicted_labels = segmentation_mymethod(train_data_matrix_kmeans,
train_labels_matrix_kmeans,
test_data_kmeans, task)
all_errors[i, 2] = util.classification_error(test_labels,
predicted_labels)
all_dice[i, 2] = util.dice_multiclass(test_labels, predicted_labels)
# Plot my own method
predicted_mask_3 = predicted_labels.reshape(im_size[0], im_size[1])
ax3 = fig.add_subplot(153)
ax3.imshow(test_shape_1, 'gray')
ax3.imshow(predicted_mask_3, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 2],
all_dice[i, 2])
ax3.set_xlabel(text_str)
ax3.set_title('Subject {}: My method'.format(sub))
ax4 = fig.add_subplot(154)
ax4.imshow(predicted_mask_3, 'viridis')
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 2],
all_dice[i, 2])
ax4.set_xlabel(text_str)
ax4.set_title('Subject {}: My method'.format(sub))
ax5 = fig.add_subplot(155)
ax5.imshow(test_shape_1, 'gray')
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 2],
all_dice[i, 2])
ax5.set_xlabel(text_str)
ax5.set_title('Subject {}: My method'.format(sub))
#select certain data:
train_data_matrix = all_data_matrix[:, :, train_subjects]
train_data_matrix = train_data_matrix[:, features, :]
test_data = test_data[:, features]
train_labels_matrix = train_labels_matrix[:, train_subjects]
#predict test data labels
predicted_labels, predicted_labels2_atlas = seg.segmentation_combined_atlas(
train_labels_matrix)
predicted_labels, predicted_labels2_knn = seg.segmentation_combined_knn(
train_data_matrix, train_labels_matrix, test_data)
#calculate error and dice
dice_atlas = util.dice_multiclass(test_labels, predicted_labels2_atlas)
err_atlas = util.classification_error(test_labels, predicted_labels2_atlas)
dice_knn = util.dice_multiclass(test_labels, predicted_labels2_knn)
err_knn = util.classification_error(test_labels, predicted_labels2_knn)
#needed for plotting the 'real' data and the predicted
test_shape = test_labels.reshape(im_size[0], im_size[1])
#Plot for combined atlas
predicted_mask_atlas = predicted_labels2_atlas.reshape(im_size[0], im_size[1])
fig, ax = plt.subplots()
ax.imshow(test_shape, 'gray')
ax.imshow(predicted_mask_atlas, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(err_atlas, dice_atlas)
ax.set_xlabel(text_str)
features = [1,4]
train_data,_ = seg.normalize_data(train_data[:, features])
test_data ,_= seg.normalize_data(test_data[:,features])
all_data_matrix, _ = seg.normalize_data(all_data_matrix[:, features, :])
#predicted_train = seg.kmeans_clustering(train_data, K=4)
if (task == 'tissue'):
k = 4
else:
k = 2
kmeans_cost, train_predicted, w_final = prj.kmeans(train_data, train_labels k, mu = 0.1, num_iter = 5)
dice = util.dice_multiclass(train_labels, train_predicted)
error = util.classification_error(train_labels, train_predicted)
print("Dice score is {:.2f}".format(dice))
print("Error is {:.2f}".format(error))
#Use my method
#predicted_labels = prj.segmentation_mymethod(train_data, train_labels, test_data, task='tissue')
pred_labels_kmeans = prj.predicted_kmeans_test(w_final, test_data).T
_, pred_labels_cat = seg.segmentation_combined_atlas(train_labels, combining='mode')
_, pred_labels_cnn = seg.segmentation_combined_knn(all_data_matrix, all_labels_matrix, test_data, k=1)
pred_labels_cat = pred_labels_cat.T
pred_labels_cnn = pred_labels_cnn.T
concat_labels = np.vstack((pred_labels_kmeans, pred_labels_cat, pred_labels_cnn)).T
def segmentation_demo():
train_subject = 1
test_subject = 2
train_slice = 1
test_slice = 1
task = 'brain'
#Load data
train_data, train_labels, train_feature_labels = util.create_dataset(
train_subject, train_slice, task)
test_data, test_labels, test_feature_labels = util.create_dataset(
test_subject, test_slice, task)
predicted_labels = seg.segmentation_atlas(None, train_labels, None)
err = util.classification_error(test_labels, predicted_labels)
dice = util.dice_overlap(test_labels, predicted_labels)
#Display results
true_mask = test_labels.reshape(240, 240)
predicted_mask = predicted_labels.reshape(240, 240)
# fig = plt.figure(figsize=(8,8))
# ax1 = fig.add_subplot(111)
# ax1.imshow(true_mask, 'gray')
# ax1.imshow(predicted_mask, 'viridis', alpha=0.5)
# print('Subject {}, slice {}.\nErr {}, dice {}'.format(test_subject, test_slice, err, dice))
## Compare methods
num_images = 5
num_methods = 3
im_size = [240, 240]
all_errors = np.empty([num_images, num_methods])
all_errors[:] = np.nan
all_dice = np.empty([num_images, num_methods])
all_dice[:] = np.nan
all_subjects = np.arange(num_images)
train_slice = 1
task = 'brain'
all_data_matrix = np.empty(
[train_data.shape[0], train_data.shape[1], num_images])
all_labels_matrix = np.empty([train_labels.size, num_images], dtype=bool)
#Load datasets once
print('Loading data for ' + str(num_images) + ' subjects...')
for i in all_subjects:
sub = i + 1
train_data, train_labels, train_feature_labels = util.create_dataset(
sub, train_slice, task)
all_data_matrix[:, :, i] = train_data
all_labels_matrix[:, i] = train_labels.flatten()
print('Finished loading data.\nStarting segmentation...')
#Go through each subject, taking i-th subject as the test
for i in np.arange(num_images):
sub = i + 1
#Define training subjects as all, except the test subject
train_subjects = all_subjects.copy()
train_subjects = np.delete(train_subjects, i)
train_data_matrix = all_data_matrix[:, :, train_subjects]
train_labels_matrix = all_labels_matrix[:, train_subjects]
test_data = all_data_matrix[:, :, i]
test_labels = all_labels_matrix[:, i]
test_shape_1 = test_labels.reshape(im_size[0], im_size[1])
fig = plt.figure(figsize=(15, 5))
predicted_labels = seg.segmentation_combined_atlas(train_labels_matrix)
all_errors[i, 0] = util.classification_error(test_labels,
predicted_labels)
all_dice[i, 0] = util.dice_overlap(test_labels, predicted_labels)
predicted_mask_1 = predicted_labels.reshape(im_size[0], im_size[1])
ax1 = fig.add_subplot(131)
ax1.imshow(test_shape_1, 'gray')
ax1.imshow(predicted_mask_1, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 0],
all_dice[i, 0])
ax1.set_xlabel(text_str)
ax1.set_title('Subject {}: Combined atlas'.format(sub))
predicted_labels = seg.segmentation_combined_knn(
train_data_matrix, train_labels_matrix, test_data)
all_errors[i, 1] = util.classification_error(test_labels,
predicted_labels)
all_dice[i, 1] = util.dice_overlap(test_labels, predicted_labels)
predicted_mask_2 = predicted_labels.reshape(im_size[0], im_size[1])
ax2 = fig.add_subplot(132)
ax2.imshow(test_shape_1, 'gray')
ax2.imshow(predicted_mask_2, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 1],
all_dice[i, 1])
ax2.set_xlabel(text_str)
ax2.set_title('Subject {}: Combined k-NN'.format(sub))
predicted_labels = segmentation_mymethod(train_data_matrix,
train_labels_matrix,
test_data, task)
all_errors[i, 2] = util.classification_error(test_labels,
predicted_labels)
all_dice[i, 2] = util.dice_overlap(test_labels, predicted_labels)
predicted_mask_3 = predicted_labels.reshape(im_size[0], im_size[1])
ax3 = fig.add_subplot(133)
ax3.imshow(test_shape_1, 'gray')
ax3.imshow(predicted_mask_3, 'viridis', alpha=0.5)
text_str = 'Err {:.4f}, dice {:.4f}'.format(all_errors[i, 2],
all_dice[i, 2])
ax3.set_xlabel(text_str)
ax3.set_title('Subject {}: My method'.format(sub))
