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How to change the dimensions of MRIs (in Nifti format) in python?

I have 700 MRIs. each MRI with the shape (170,256,256,1). and I need to change these MRIs to the shape (no of the MRIs (700), 256,256,1). so I can feed them to the deep learning segmentation model.
for img in range(len(image_names_subset)):
image_dataset = nib.load(image_names_subset[img]) #read the nifti images
image_dataset=nib.squeeze_image(image_dataset) #squeeze the image
image_dataset = processing.conform(image_dataset, out_shape=(256, 256, 1)) #change the dimensions
image_dataset.shape
(256, 256, 1)
after I run this code the number of images does not exist. I got (256,256,1) and what I expected is (700,256,256,1).
Any clarification will help

what is the use of expand_dims in image processing?

I saw a face detection model which consists of the below function. but I could not understand what is the use of the expand_dims function. can anyone explain me what it is and why we are using ?
def get_embedding(model,face_pixels):
face_pixels=face_pixels.astype('float32')
mean, std=face_pixels.mean(),face_pixels.std()
face_pixels=(face_pixels-mean)/std
samples=expand_dims(face_pixels,axis=0)
yhat=model.predict(samples)
return yhat[0]

tf.keras.Conv2D layers expect input with 4D shape:
(n_samples, height, width, channels)
Most libraries that load images will load in 3D like this:
(height, width, channels)
By using np.expand_dims(image, axis=0) or tf.expand_dims(image, axis=0), you add a batch dimension at the beginning, effectively turning your data in the 4D format the Keras needs for Conv2D layers. For instance:
(224, 224, 3)
to:
(1, 224, 224, 3)
If you give Conv2D 3D data, it will give something like this:
ValueError: Error when checking input: expected conv2d_19_input to have 4 dimensions, but got array with shape (60000, 28, 28)

Is it possible to feed the pretrained Inception model (tensorflow 2.0/Keras) with 2D grayscale images?

According to Keras 2.0 documentation, in relation to the input shape of the images that can be fed to the pretrained inception model:
input_shape: optional shape tuple, only to be specified if include_top
is False (otherwise the input shape has to be (299, 299, 3) (with
'channels_last' data format) or (3, 299, 299) (with 'channels_first'
data format). It should have exactly 3 inputs channels, and width and
height should be no smaller than 75. E.g. (150, 150, 3) would be one
valid value.
However, I am dealing with grayscale image which are 2D. How I should deal with this situation?

You can copy the grayscale image 3 times for a pseudoRGB image
import numpy as np
# img=np.zeros((224,224))
If your image is of shape length 2, only width and height you will first need to add an extra dimension:
img = np.expand_dims(img,-1)
The you repeat this last dimension 3 times:
img = np.repeat(img,3,2)
print(img.shape)
# (224,224,3)

Difference between 3D-tensor and 4D-tensor for images input of DL Keras framework

By convention an image tensor is always 3D : One dimension for its height, one for its width and a third one for its color channel. Its shape looks like (height, width, color).
For instance a batch of 128 color images of size 256x256 could be stored in a 4D-tensor of shape (128, 256, 256, 3). The color channel represents here RGB colors. Another example with batch of 128 grayscale images stored in a 4D-tensor of shape (128, 256, 256, 1). The color could be coded as 8-bit integers.
For the second example, the last dimension is a vector containing only one element. It is then possible to use a 3D-tensor of shape (128, 256, 256,) instead.
Here comes my question : I would like to know if there is a difference between using a 3D-tensor rather than a 4D-tensor as the training input of a deep-learning framework using keras.
EDIT : My input layer is a conv2D

I you take a look at the Keras documentation of the conv2D layer here you will see that the shape of the input tensor must be 4D.
conv2D layer input shape
4D tensor with shape: (batch, channels, rows, cols) if data_format is "channels_first" or 4D tensor with shape: (batch, rows, cols, channels) if data_format is "channels_last".
So the 4th dimension of the shape is mandatory, even if it is only "1" as for a grayscaled image.
So in fact, it is not a matter of performance gain nor lack of simplicity, it's only the mandatory input argument's shape.
Hope it answers your question.

keras reshape input image to work with CNN

There are other post with similar questions but none of the answers are helping me. I´m new to this CNN world.
I followed this tutorial for training a CNN with Keras using theano as BackEnd with the MNIST dataset. Now I want to pass to the CNN my own jpg image but I dont know how to reshape it. Can you help me please? Im super new at this.
So far, I tried this to reshape
image = np.expand_dims(image, axis=0) image = preprocess_input(image)
but get the following error when predicting:
ValueError: Error when checking : expected conv2d_1_input to have shape (None, 1, 28, 28) but got array with shape (1, 3, 28, 28)
As you can see, my CNN uses width = 28, height = 28 and depth =1.

Try using Numpy for reshaping. Since, you have been using a 2D-Convolutional model:
image = np.reshape(image, (28, 1, 28, 1))

The error message shows the network expects the image shape is 1*28*28, but your input is in 3*28*28. I guess the image you input is a color image, 3 channels(RGB), while the network expects a gray image, one channel.
When you call opencv to read image, please use code below.
img = cv2.imread(imgfile, cv2.IMREAD_GRAYSCALE)

simply use
'''image = np.reshape(len(image), (28,28, 1))'''