Sai Rajesh Vanimireddy
Posted on January 2, 2020
A lot of effort in solving any machine learning problem goes into preparing the data. In this tutorial, we will see how to load and preprocess/augment data from a non-trivial dataset. The various image format classes give full or selective access to the header (meta) information and access to the image data is made available via NumPy arrays.
To run this tutorial, please make sure the following packages are installed:
Parameter:
Download the sample nibabel files from my github which is in nii.giz form
Import Libraries:
import os
import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
}
Directory for MRI data
Importing Datasets: dirName = r"file_path"
function to read data from dirName
def FileRead(file_path):
nii = nib.load(file_path)
data = nii.get_data()
#if (np.shape(np.shape(data))[0] == 3): #If channel data not present
# data = np.expand_dims(data, 3)
return data
function returns a 2 dimensional numpy array from a 3 dimensional data
def Nifti3Dto2D(Nifti3D):
Nifti3DWOChannel = Nifti3D#[:,:,:,0] #Considering there is only one channel info
Nifti2D = Nifti3DWOChannel.reshape(np.shape(Nifti3DWOChannel)[0], np.shape(Nifti3DWOChannel)[1] * np.shape(Nifti3DWOChannel)[2])
return Nifti2D
function returns a 1 dimensional numpy array from a 2 dimensional array
def Nifti2Dto1D(Nifti2D):
Nifti1D = Nifti2D.reshape(np.shape(Nifti2D)[0] * np.shape(Nifti2D)[1])
return Nifti1D
reshapes a 1 dimensional array to 2 dimensional data
def Nifti1Dto2D(Nifti1D, height):
Nifti2D = Nifti1D.reshape(height,int(np.shape(Nifti1D)[0]/height))
return Nifti2D
reshapes 2 dimensional array to 3 dimensional data
def Nifti2Dto3D(Nifti2D):
Nifti3DWOChannel = Nifti2D.reshape(np.shape(Nifti2D)[0],np.shape(Nifti2D)[0],np.shape(Nifti2D)[1]//np.shape(Nifti2D)[0])
#Nifti3D = np.expand_dims(Nifti3DWOChannel, 3)
return Nifti3DWOChannel
normalize data between o-1
def normalize(x):
min_val = np.min(x)
max_val = np.max(x)
x = (x-min_val) / (max_val-min_val)
return x
To copy data to local storage
def FileSave(data, file_path):
nii = nib.Nifti1Image(data, np.eye(4))
nib.save(nii, file_path)
returns list of files from the given dirName
def getListOfFiles(dirName):
# creating a list of file and sub directories
listOfFile = os.listdir(dirName)
allFiles = []
# Iterate over entries
for entry in listOfFile:
# Creating full path
fullPath = os.path.join(dirName, entry)
if os.path.isdir(fullPath):
allFiles = allFiles + getListOfFiles(fullPath)
else:
allFiles.append(fullPath)
return allFiles
Takes list of files and creates 1 dimension and 2 dimension data from MR data
def main():
listOfFiles = getListOfFiles(dirName)
twod_data = []# to save two dimensional data
oned_data = []
listOfFiles = []
for (dirpath, dirnames, filenames) in os.walk(dirName):
listOfFiles += [os.path.join(dirpath, file) for file in filenames]
# listOfFiles_under.append(listOfFiles_under)
#print(listOfFiles_under)
for element in listOfFiles:
print(element)
red_files = FileRead(element)
twod_array = Nifti3Dto2D(red_files)
twod_data.append(twod_array)
#print(twod_unsam)
oned_array= Nifti2Dto1D(twod_array)
oned_data.append(oned_array)
oned_data = np.asarray(oned_data, dtype=np.float64, order='C')
twod_data = np.asarray(twod_data, dtype=np.float64, order='C')
return oned_data, twod_data
oned_data, twod_data = main()
for visualization
data_vis = Nifti2Dto3D(Nifti1Dto2D(oned_data[0,:], 256))
#plt.imshow(data_vis[20,:,:], cmap='gray')
plt.imshow(data_vis[:,:,20], cmap='gray')
Output for one slice image
Posted on January 2, 2020
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