3D-Medical-Imaging-Preprocessing-All-you-need
This Repo contains the Preprocessing Code for 3D Medical Imaging
From the last year of my undergrad studies I was very queries about Biomedical Imaging. But until the starting my master I don't have the chance to go deep into medical imaging. Like most people at the beginning, I also suffered and was a bit confused about a few things. In this notebook, I will try to easily explain/show commonly used Preprocessing in Medical Imaging especially with 3D Nifti.
In this tutorial we will be using Public Abdomen Dataset From: Multi-Atlas Labeling Beyond the Cranial Vault - Workshop and Challenge Link: https://www.synapse.org/#!Synapse:syn3193805/wiki/217789
Reference: https://github.com/DLTK/DLTK
In this Notebook we will cover
- Reading Nifti Data and ploting
- Different Intensity Normalization Approaches
- Resampling 3D CT data
- Cropping and Padding CT data
- Histogram equalization
- Maximum Intensity Projection (MIP)
if You want know about MRI Histogram Matching, Histogram Equalization and Registration, You could have a look to my repo
- https://github.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet
- https://github.com/fitushar/Registration-as-Data-Augumentation-for-CT--DATA
To Learn about Segmentation
-
Brain Tissue Segmentation, 3D : https://github.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet
-
Chest-Abdomen-Pelvis (Segmentation) 3D DenseVnet :https://github.com/fitushar/DenseVNet3D_Chest_Abdomen_Pelvis_Segmentation_tf2
Libraries need
* SimpleITK
* numpy
* scipy
* skimage
* cv2Reading Nifti Data and ploting
ct_path='D:/Science/Github/3D-Medical-Imaging-Preprocessing-All-you-need/Data/img0001.nii.gz'
ct_label_path='D:/Science/Github/3D-Medical-Imaging-Preprocessing-All-you-need/Data/label0001.nii.gz'
# CT
img_sitk = sitk.ReadImage(ct_path, sitk.sitkFloat32) # Reading CT
image = sitk.GetArrayFromImage(img_sitk) #Converting sitk_metadata to image Array
# Mask
mask_sitk = sitk.ReadImage(ct_label_path,sitk.sitkInt32) # Reading CT
mask = sitk.GetArrayFromImage(mask_sitk)#Converting sitk_metadata to image Array
Intensity Normalization
def normalise(image):
# normalise and clip images -1000 to 800
np_img = image
np_img = np.clip(np_img, -1000., 800.).astype(np.float32)
return np_img
def whitening(image):
"""Whitening. Normalises image to zero mean and unit variance."""
image = image.astype(np.float32)
mean = np.mean(image)
std = np.std(image)
if std > 0:
ret = (image - mean) / std
else:
ret = image * 0.
return ret
def normalise_zero_one(image):
"""Image normalisation. Normalises image to fit [0, 1] range."""
image = image.astype(np.float32)
minimum = np.min(image)
maximum = np.max(image)
if maximum > minimum:
ret = (image - minimum) / (maximum - minimum)
else:
ret = image * 0.
return ret
def normalise_one_one(image):
"""Image normalisation. Normalises image to fit [-1, 1] range."""
ret = normalise_zero_one(image)
ret *= 2.
ret -= 1.
return ret
Resampling
def resample_img(itk_image, out_spacing=[2.0, 2.0, 2.0], is_label=False):
# resample images to 2mm spacing with simple itk
original_spacing = itk_image.GetSpacing()
original_size = itk_image.GetSize()
out_size = [
int(np.round(original_size[0] * (original_spacing[0] / out_spacing[0]))),
int(np.round(original_size[1] * (original_spacing[1] / out_spacing[1]))),
int(np.round(original_size[2] * (original_spacing[2] / out_spacing[2])))]
resample = sitk.ResampleImageFilter()
resample.SetOutputSpacing(out_spacing)
resample.SetSize(out_size)
resample.SetOutputDirection(itk_image.GetDirection())
resample.SetOutputOrigin(itk_image.GetOrigin())
resample.SetTransform(sitk.Transform())
resample.SetDefaultPixelValue(itk_image.GetPixelIDValue())
if is_label:
resample.SetInterpolator(sitk.sitkNearestNeighbor)
else:
resample.SetInterpolator(sitk.sitkBSpline)
return resample.Execute(itk_image)
Crop or Padding
def resize_image_with_crop_or_pad(image, img_size=(64, 64, 64), **kwargs):
"""Image resizing. Resizes image by cropping or padding dimension
to fit specified size.
Args:
image (np.ndarray): image to be resized
img_size (list or tuple): new image size
kwargs (): additional arguments to be passed to np.pad
Returns:
np.ndarray: resized image
"""
assert isinstance(image, (np.ndarray, np.generic))
assert (image.ndim - 1 == len(img_size) or image.ndim == len(img_size)), \
'Example size doesnt fit image size'
# Get the image dimensionality
rank = len(img_size)
# Create placeholders for the new shape
from_indices = [[0, image.shape[dim]] for dim in range(rank)]
to_padding = [[0, 0] for dim in range(rank)]
slicer = [slice(None)] * rank
# For each dimensions find whether it is supposed to be cropped or padded
for i in range(rank):
if image.shape[i] < img_size[i]:
to_padding[i][0] = (img_size[i] - image.shape[i]) // 2
to_padding[i][1] = img_size[i] - image.shape[i] - to_padding[i][0]
else:
from_indices[i][0] = int(np.floor((image.shape[i] - img_size[i]) / 2.))
from_indices[i][1] = from_indices[i][0] + img_size[i]
# Create slicer object to crop or leave each dimension
slicer[i] = slice(from_indices[i][0], from_indices[i][1])
# Pad the cropped image to extend the missing dimension
return np.pad(image[slicer], to_padding, **kwargs)
