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mTOP2016_dataPreprocessingFeautreRepresentation.py
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140 lines (102 loc) · 5.8 KB
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# -*- coding: utf-8 -*-
"""
Created on Mon Aug 1 14:44:28 2016
@author: poyu
Data Pre-processing for Feature Representation
"""
import os
import nibabel as nib
import numpy as np
#def load_dataset():
def normalized_3d(inputs):
inputs=np.copy(inputs)
x_dim, y_dim, z_dim = inputs.shape
number_non_zero = np.float32(np.count_nonzero(inputs))
sum_inputs = np.float32(inputs.sum())
mean_inputs = sum_inputs/number_non_zero
input_nonzero = inputs[np.nonzero(inputs)]
std_input = np.std(input_nonzero)
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
if inputs[i,j,k] != 0 :
inputs[i,j,k] = (inputs[i,j,k]-mean_inputs)/std_input
return inputs
def createDatasetFeatureRepresentation():
boundary_t1 = [11, 167, 9, 207, 9, 153]
t1_filepaths = [os.path.join(root,name) for root, dirs, files in os.walk(os.getcwd()) for name in files if "Brain" in name and "MR_T1" in name and name.endswith("nii")]
t1_filepaths.sort()
boundary_fa = [6, 84, 5, 104, 8, 80]
fa_filepaths = [os.path.join(root,name) for root, dirs, files in os.walk(os.getcwd()) for name in files if "Brain" in name and "DT_FA" in name and name.endswith("nii")]
fa_filepaths.sort()
boundary_md = [6, 84, 5, 104, 8, 80]
md_filepaths = [os.path.join(root,name) for root, dirs, files in os.walk(os.getcwd()) for name in files if "Brain" in name and "DT_MD" in name and name.endswith("nii")]
md_filepaths.sort()
# =============================Train========================================= #
train_subject = np.array([5, 11, 17, 22, 27, 3, 10, 16, 20, 26, 4, 7, 8, 13, 25])-1
train_label = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2])
train_t1 = np.zeros((train_subject.shape[0],156,198,144), dtype = np.float32)
train_t1_scaled = train_t1
train_fa = np.zeros((train_subject.shape[0],78,99,72), dtype = np.float32)
train_fa_scaled = train_fa
train_md = np.zeros((train_subject.shape[0],78,99,72), dtype = np.float32)
train_md_scaled = train_md
count_train = 0
for sub in train_subject:
img_t1 = nib.load(t1_filepaths[sub])
t1_data = img_t1.get_data()
train_t1[count_train,:] = t1_data[boundary_t1[0]:boundary_t1[1],boundary_t1[2]:boundary_t1[3],boundary_t1[4]:boundary_t1[5]]
train_t1_scaled[count_train,:] = normalized_3d(train_t1[count_train,:])
img_fa = nib.load(fa_filepaths[sub])
fa_data = img_fa.get_data()
train_fa[count_train,:] = fa_data[boundary_fa[0]:boundary_fa[1],boundary_fa[2]:boundary_fa[3],boundary_fa[4]:boundary_fa[5]]
train_fa_scaled[count_train,:] = normalized_3d(train_fa[count_train,:])
img_md = nib.load(md_filepaths[sub])
md_data = img_md.get_data()
train_md[count_train,:] = md_data[boundary_md[0]:boundary_md[1],boundary_md[2]:boundary_md[3],boundary_md[4]:boundary_md[5]]
train_md_scaled[count_train,:] = normalized_3d(train_md[count_train,:])
count_train = count_train+1
# ==============================Test========================================= #
test_subject = np.array([1, 2, 6, 9, 12, 14, 15, 18, 19, 21, 23, 24])-1
test_t1 = np.zeros((test_subject.shape[0],156,198,144), dtype = np.float32)
test_t1_scaled = test_t1
test_fa = np.zeros((test_subject.shape[0],78,99,72), dtype = np.float32)
test_fa_scaled = test_fa
test_md = np.zeros((test_subject.shape[0],78,99,72), dtype = np.float32)
test_md_scaled = test_md
count_test = 0
for sub in test_subject:
img_t1 = nib.load(t1_filepaths[sub])
t1_data = img_t1.get_data()
test_t1[count_test,:] = t1_data[boundary_t1[0]:boundary_t1[1],boundary_t1[2]:boundary_t1[3],boundary_t1[4]:boundary_t1[5]]
test_t1_scaled[count_test,:] = normalized_3d(test_t1[count_test,:] )
img_fa = nib.load(fa_filepaths[sub])
fa_data = img_fa.get_data()
test_fa[count_test,:] = fa_data[boundary_fa[0]:boundary_fa[1],boundary_fa[2]:boundary_fa[3],boundary_fa[4]:boundary_fa[5]]
test_fa_scaled[count_test,:] = normalized_3d(test_fa[count_test,:])
img_md = nib.load(md_filepaths[sub])
md_data = img_md.get_data()
test_md[count_test,:] = md_data[boundary_md[0]:boundary_md[1],boundary_md[2]:boundary_md[3],boundary_md[4]:boundary_md[5]]
test_md_scaled[count_test,:] = normalized_3d(test_md[count_test,:])
count_test = count_test+1
print train_t1_scaled.shape, train_fa_scaled.shape, train_md_scaled.shape
print test_t1_scaled.shape, test_fa_scaled.shape, test_md_scaled.shape
if not os.path.exists(os.path.join(os.getcwd(), 'numpy_data')):
os.makedirs(os.path.join(os.getcwd(), 'numpy_data'))
#np.save('X_T1_train.npy', train_t1)
np.save(os.path.join(os.getcwd(),'numpy_data','X_T1_scaled_train.npy'), train_t1_scaled)
#np.save('X_FA_train.npy', train_fa)
np.save(os.path.join(os.getcwd(),'numpy_data','X_FA_scaled_train.npy'), train_fa_scaled)
#np.save('X_MD_train.npy', train_md)
np.save(os.path.join(os.getcwd(),'numpy_data','X_MD_scaled_train.npy'), train_md_scaled)
np.save(os.path.join(os.getcwd(),'numpy_data','y_train.npy'), train_label)
#np.save('X_T1_test.npy', test_t1)
np.save(os.path.join(os.getcwd(),'numpy_data','X_T1_scaled_test.npy'), test_t1_scaled)
#np.save('X_FA_test.npy', test_fa)
np.save(os.path.join(os.getcwd(),'numpy_data','X_FA_scaled_test.npy'), test_fa_scaled)
#np.save('X_MD_test.npy', test_md)
np.save(os.path.join(os.getcwd(),'numpy_data','X_MD_scaled_test.npy'), test_md_scaled)
def main():
createDatasetFeatureRepresentation()
if __name__ == "__main__":
main()