Source code for delira.utils.imageops

import SimpleITK as sitk
import numpy as np
from scipy.ndimage import zoom

from delira.utils.decorators import dtype_func

sitk_img_func = dtype_func(sitk.Image)

[docs]def calculate_origin_offset(new_spacing, old_spacing): """ Calculates the origin offset of two spacings Parameters ---------- new_spacing : list or np.ndarray or tuple new spacing old_spacing : list or np.ndarray or tuple old spacing Returns ------- np.ndarray origin offset """ return np.subtract(new_spacing, old_spacing) / 2
[docs]@sitk_img_func def sitk_resample_to_spacing(image, new_spacing=(1.0, 1.0, 1.0), interpolator=sitk.sitkLinear, default_value=0.): """ Resamples SITK Image to a given spacing Parameters ---------- image : SimpleITK.Image image which should be resampled new_spacing : list or np.ndarray or tuple target spacing interpolator : Any implements the actual interpolation default_value : float default value Returns ------- SimpleITK.Image resampled Image with target spacing """ zoom_factor = np.divide(image.GetSpacing(), new_spacing) new_size = np.asarray(np.ceil(np.round(np.multiply( zoom_factor, image.GetSize()), decimals=5)), dtype=np.int16) offset = calculate_origin_offset(new_spacing, image.GetSpacing()) reference_image = sitk_new_blank_image(size=new_size, spacing=new_spacing, direction=image.GetDirection(), origin=image.GetOrigin() + offset, default_value=default_value) return sitk_resample_to_image(image, reference_image, interpolator=interpolator, default_value=default_value)
[docs]@sitk_img_func def sitk_resample_to_image(image, reference_image, default_value=0., interpolator=sitk.sitkLinear, transform=None, output_pixel_type=None): """ Resamples Image to reference image Parameters ---------- image : SimpleITK.Image the image which should be resampled reference_image : SimpleITK.Image the resampling target default_value : float default value interpolator : Any implements the actual interpolation transform : Any (default: None) transformation output_pixel_type : Any (default:None) type of output pixels Returns ------- SimpleITK.Image resampled image """ if transform is None: transform = sitk.Transform() transform.SetIdentity() if output_pixel_type is None: output_pixel_type = image.GetPixelID() resample_filter = sitk.ResampleImageFilter() resample_filter.SetInterpolator(interpolator) resample_filter.SetTransform(transform) resample_filter.SetOutputPixelType(output_pixel_type) resample_filter.SetDefaultPixelValue(default_value) resample_filter.SetReferenceImage(reference_image) return resample_filter.Execute(image)
[docs]def sitk_new_blank_image(size, spacing, direction, origin, default_value=0.): """ Create a new blank image with given properties Parameters ---------- size : list or np.ndarray or tuple new image size spacing : list or np.ndarray or tuple spacing of new image direction : new image's direction origin : new image's origin default_value : float new image's default value Returns ------- SimpleITK.Image Blank image with given properties """ image = sitk.GetImageFromArray( np.ones(size, dtype=np.float).T * default_value) image.SetSpacing(spacing) image.SetDirection(direction) image.SetOrigin(origin) return image
[docs]@sitk_img_func def sitk_resample_to_shape(img, x, y, z, order=1): """ Resamples Image to given shape Parameters ---------- img : SimpleITK.Image x : int shape in x-direction y : int shape in y-direction z : int shape in z-direction order : int interpolation order Returns ------- SimpleITK.Image Resampled Image """ img_np = sitk.GetArrayFromImage(img) img_np_fixed_size = zoom(img_np, [z / img_np.shape[0], y / img_np.shape[1], x / img_np.shape[2]], order=order) return sitk.GetImageFromArray(img_np_fixed_size)
[docs]@sitk_img_func def max_energy_slice(img): """Determine the axial slice in which the image energy is max Parameters ---------- img : SimpleITK.Image given image Returns ------- int slice index """ assert img.GetDimension() == 3 return int(np.argmax(np.sum(sitk.GetArrayFromImage(img), axis=(1, 2))))
[docs]def sitk_copy_metadata(img_source, img_target): """ Copy metadata (=DICOM Tags) from one image to another Parameters ---------- img_source : SimpleITK.Image Source image img_target : SimpleITK.Image Source image Returns ------- SimpleITK.Image Target image with copied metadata """ for k in img_source.GetMetaDataKeys(): img_target.SetMetaData(k, img_source.GetMetaData(k)) return img_target
[docs]@sitk_img_func def bounding_box(mask, margin=None): """Calculate bounding box coordinates of binary mask Parameters ---------- mask : SimpleITK.Image Binary mask margin : int, default: None margin to be added to min/max on each dimension Returns ------- tuple bounding box coordinates of the form (xmin, xmax, ymin, ymax, zmin, zmax) """ # mask_arr is in z, y, x order mask_arr = sitk.GetArrayFromImage(mask) nz = np.where(mask_arr != 0) lower = [np.min(nz[0]), np.min(nz[1]), np.min(nz[2])] upper = [np.max(nz[0]), np.max(nz[1]), np.max(nz[2])] if margin is not None: for axis in range(3): # make sure lower bound with margin is valid if lower[axis] - margin >= 0: lower[axis] -= margin else: lower[axis] = 0 # make sure upper bound with margin is valid if upper[axis] + margin <= mask_arr.shape[axis] - 1: upper[axis] += margin else: upper[axis] = mask_arr.shape[axis] - 1 bbox = lower[0], upper[0], lower[1], upper[1], lower[2], upper[2] return bbox