image_io

Image IO

# scikit-image

import skimage

vol=skimage.io.imread( 't.mhd' )

# Simple ITK

import SimpleITK as sitk

vol =sitk.ReadImage('t.mhd')

Sample MHD/ MHA

OjectType = Image

NDims = 3

BinaryData = True

BinaryDataByteOrderMSB = False

CompressedData = False

TransformMatrix = 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0

Offset = 0.0 0.0 0.0

CenterOfRotation = 0.0 0.0 0.0

AnatomicalOrientation = RAS

ElementSpacing = 1.49479 1.49479 2.3125

DimSize = 668 668 980

ElementType = MET_SHORT

ElementDataFile = data.raw

Viewpoints

https://kitware.github.io/vtk-examples/site/Cxx/VisualizationAlgorithms/AnatomicalOrientation/

Torch

.nii in Python

import nibabel as nib

hd = nib.load( path_to_single_nii )

k = 'pixdim'

print( 'Pixel spacing', hd.header[k] )

load_nii( path_to_single_nii )

<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'

sizeof_hdr : 348

data_type : b''

db_name : b''

extents : 0

session_error : 0

regular : b'r'

dim_info : 0

dim : [ 3 512 512 42 1 1 1 1]

intent_p1 : 0.0

intent_p2 : 0.0

intent_p3 : 0.0

intent_code : none

datatype : int16

bitpix : 16

slice_start : 0

pixdim : [-1. 0.782 0.782 8. 0. 0. 0. 0. ]

vox_offset : 0.0

scl_slope : nan

scl_inter : nan

slice_end : 0

slice_code : unknown

xyzt_units : 10

cal_max : 0.0

cal_min : 0.0

slice_duration : 0.0

toffset : 0.0

glmax : 0

glmin : 0

descrip : b'Time=134609.000'

aux_file : b''

qform_code : scanner

sform_code : scanner

quatern_b : 0.0

quatern_c : 1.0

quatern_d : 0.0

qoffset_x : 201.757

qoffset_y : -199.79768

qoffset_z : -1296.7

srow_x : [ -0.782 -0. 0. 201.757]

srow_y : [ 0. 0.782 0. -199.79768]

srow_z : [ 0. 0. 8. -1296.7]

intent_name : b''

magic : b'n+1'