I have a python code that reads NIFTI images using SimpleITK library. Then it converts that images into a Numpy Array. Then, I extend the Numpy Array into a list.
I have 20 FLAIR.nii.gz files. Each of them have 48 slices.
When I have all the 48 slices of all the 20 patients, I convert the list into a Numpy Array.
I do it this way because I'm newbie with Python and I don't know any other way to do it.
The code is:
import os
import SimpleITK as sitk
import numpy as np
flair_dataset = []
# For each patient directory
# data_path is a list with all of the patient's directory.
for i in data_path:
img_path = os.path.join(file_path, i, 'pre')
mask_path = os.path.join(file_path, i)
for name in glob.glob(img_path+'/FLAIR*'):
# Reads images using SimpleITK.
brain_image = sitk.ReadImage(name)
# Get a numpy array from a SimpleITK Image.
brain_array = sitk.GetArrayFromImage(brain_image)
flair_dataset.extend(brain_array)
if debug:
print('brain_image size: ', brain_image.GetSize())
print('brain_array Shape: ', brain_array.shape)
print('flair_dataset length:', len(flair_dataset))
print('flair_dataset length: ', len(flair_dataset))
print('flair_dataset[1] type: ', print(type(flair_dataset[1])))
print('flair_dataset[1] shape: ', print(flair_dataset[1].shape))
flair_array = np.array(flair_dataset)
print('flair_array.shape: ', flair_array.shape)
print('flair_array.dtype: ', flair_array.dtype)
This code generates this output (all FLAIR.nii.gz files have the same shape):
data_path = ['68', '55', '50', '61', '63', '52', '51', '60', '67', '58', '59', '53', '69', '64', '56', '65', '54', '62', '66', '57']
patient_data_path = 68
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 48
Mask list length: 48
patient_data_path = 55
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 96
Mask list length: 96
patient_data_path = 50
brain_image size: (256, 232, 48)
brain_array Shape: (48, 232, 256)
flair_dataset length: 144
WMH image Size: (256, 232, 48)
WMH array Shape: (48, 232, 256)
Mask list length: 144
patient_data_path = 61
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 192
Mask list length: 192
patient_data_path = 63
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 240
Mask list length: 240
patient_data_path = 52
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 288
Mask list length: 288
patient_data_path = 51
brain_image size: (256, 232, 48)
brain_array Shape: (48, 232, 256)
flair_dataset length: 336
WMH image Size: (256, 232, 48)
WMH array Shape: (48, 232, 256)
Mask list length: 336
patient_data_path = 60
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 384
Mask list length: 384
patient_data_path = 67
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 432
Mask list length: 432
patient_data_path = 58
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 480
Mask list length: 480
patient_data_path = 59
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 528
Mask list length: 528
patient_data_path = 53
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 576
Mask list length: 576
patient_data_path = 69
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 624
Mask list length: 624
patient_data_path = 64
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 672
Mask list length: 672
patient_data_path = 56
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 720
Mask list length: 720
patient_data_path = 65
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 768
Mask list length: 768
patient_data_path = 54
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 816
Mask list length: 816
patient_data_path = 62
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 864
Mask list length: 864
patient_data_path = 66
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 912
Mask list length: 912
patient_data_path = 57
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
flair_dataset length: 960
Mask list length: 960
The final output from the code is:
flair_dataset length: 960
mask_dataset length: 960
flair_dataset[1] type: <class 'numpy.ndarray'>
flair_dataset[1] shape: (256, 232)
flair_array.shape: (960,)
flair_array.dtype: object
My problem:
I don't understand why flair_array has this shape: (960,).
flair_array dtype is object.
I have tried the same code, without changing anything, and it works perfectly. It has 20 patients also, and 48 slices for each FLAIR.nii.gz file also.
Its output:
data_path = ['39', '31', '2', '23', '35', '29', '17', '49', '27', '8', '33', '4', '19', '41', '37', '11', '25', '6', '0', '21']
patient_data_path = 39
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 48
Mask list length: 48
patient_data_path = 31
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 96
Mask list length: 96
patient_data_path = 2
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 144
Mask list length: 144
patient_data_path = 23
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 192
Mask list length: 192
patient_data_path = 35
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 240
Mask list length: 240
patient_data_path = 29
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 288
Mask list length: 288
patient_data_path = 17
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 336
Mask list length: 336
patient_data_path = 49
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 384
Mask list length: 384
patient_data_path = 27
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 432
Mask list length: 432
patient_data_path = 8
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 480
Mask list length: 480
patient_data_path = 33
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 528
Mask list length: 528
patient_data_path = 4
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 576
Mask list length: 576
patient_data_path = 19
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 624
Mask list length: 624
patient_data_path = 41
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 672
Mask list length: 672
patient_data_path = 37
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 720
Mask list length: 720
patient_data_path = 11
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 768
Mask list length: 768
patient_data_path = 25
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 816
Mask list length: 816
patient_data_path = 6
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 864
Mask list length: 864
patient_data_path = 0
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 912
Mask list length: 912
patient_data_path = 21
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
flair_dataset length: 960
Mask list length: 960
This is the final output for this dataset:
flair_dataset length: 960
mask_dataset length: 960
flair_dataset[1] type: <class 'numpy.ndarray'>
flair_dataset[1] shape: (240, 240)
flair_array.shape: (960, 240, 240)
flair_array.dtype: float32
With this second dataset the flair_array is float32.
Why the first flair_array shape is (960,)?
UPDATE:
In both datasets, brain_array.dtype is float32 always.
In one case
flair_array.shape: (960,)
flair_array.dtype: object
in the other
flair_array.shape: (960, 240, 240)
flair_array.dtype: float32
You make these with:
flair_array = np.array(flair_dataset)
If all the elements of flair_dataset have the same shape, it can create a multidimensional array from them.
But if one or more of the arrays in the list differ in shape, it has to give up on the multidimensional goal, and instead just makes an object dtype array, which is very much like a list - in contains references to the original arrays.
In the original list most of the elements are
brain_image size: (232, 256, 48)
brain_array Shape: (48, 256, 232)
but I also see some
brain_image size: (256, 232, 48)
brain_array Shape: (48, 232, 256)
In the second set all are
brain_image size: (240, 240, 48)
brain_array Shape: (48, 240, 240)
When people ask about a (n,) shape, when they expect (n,m,p), I suspect the first has an object dtype caused by a mix in element shapes. That's why I asked about dtype.