Scikitimage Sart¶
Description¶
A Plugin to reconstruct an image by filter back projection using the inverse radon transform from scikit-image.
Parameters
in_datasets:
visibility: datasets
dtype: "[list[],list[str]]"
description:
summary: A list of the dataset(s) to process.
verbose: A list of strings, where each string gives the name of a dataset that was either specified by a loader plugin or created as output to a previous plugin. The length of the list is the number of input datasets requested by the plugin. If there is only one dataset and the list is left empty it will default to that dataset.
default: "[]"
out_datasets:
visibility: datasets
dtype: "[list[],list[str]]"
description:
summary: A list of the dataset(s) to create.
verbose: A list of strings, where each string is a name to be assigned to a dataset output by the plugin. If there is only one input dataset and one output dataset and the list is left empty, the output will take the name of the input dataset. The length of the list is the number of output datasets created by the plugin.
default: "[]"
centre_of_rotation:
visibility: basic
dtype: "[float, str, dict{int:float}]"
description: Centre of rotation to use for the reconstruction.
default: "0.0"
example: It could be a fixed value, a dictionary of (sinogram number, value) pairs for a polynomial fit of degree 1, or a dataset name.
init_vol:
visibility: intermediate
dtype: "[None, str]"
description: Dataset to use as volume initialiser (does not currently work with preview)
default: None
example: "Type the name of the initialised dataset e.g. ['tomo']"
log:
visibility: intermediate
dtype: bool
description:
summary: Option to take the log of the data before reconstruction.
verbose: Should be set to false if you use PaganinFilter
default: "True"
example: Set to True to take the log of the data before reconstruction.
preview:
visibility: intermediate
dtype: preview
description: A slice list of required frames.
default: "[]"
force_zero:
visibility: intermediate
dtype: "[list[float,float],list[None,None]]"
description: Set any values in the reconstructed image outside of this range to zero.
default: "['None', 'None']"
example: "[0, 1]"
ratio:
visibility: intermediate
dtype: "[float, list[float, float]]"
description: Ratio of the masks diameter in pixels to the smallest edge size along given axis. If a list of two floats is given, the second value is used to fill up the area outside the mask.
default: "0.95"
log_func:
visibility: advanced
dtype: str
description: Override the default log function with a numpy statement
default: np.nan_to_num(-np.log(sino))
vol_shape:
visibility: hidden
dtype: "[str, int]"
description:
summary: Override the size of the reconstruction volume with an integer value.
verbose: When fixed, you get the dimension of the horizontal detector or you can specify any reconstruction size you like with an integer.
default: fixed
iterations:
visibility: basic
dtype: int
description: Number of iterations in the reconstruction.
default: "1"
output_size:
visibility: intermediate
dtype: "[None, int, list[int,int], str]"
description: Number of rows and columns in the reconstruction.
default: auto
filter:
visibility: intermediate
dtype: str
description: Filter used in frequency domain filtering. Ramp filter used by default. Assign None to use no filter.
options: "['ramp', 'shepp-logan', 'cosine', 'hamming', 'hann', 'None']"
default: ramp
interpolation:
visibility: intermediate
dtype: int
description: Interpolation method used in reconstruction. Methods available: linear, nearest, and cubic (cubic is slow).
options: "['linear', 'nearest', 'cubic']"
default: linear
circle:
visibility: intermediate
dtype: bool
description: Assume the reconstructed image is zero outside the inscribed circle. Also changes the default output_size to match the behaviour of radon called with circle=True.
default: "False"
image:
visibility: intermediate
dtype: "[None,list]"
description: "2D array, dtype=float, optional. Image containing an initial reconstruction estimate. Shape of this array should be (radon_image.shape[0], radon_image.shape[0]). The default is a filter backprojection using scikit.image.iradon as 'result'."
default: None
clip:
visibility: intermediate
dtype: "[list,None]"
description: "length-2 sequence of floats. Force all values in the reconstructed tomogram to lie in the range [clip[0], clip[1]]."
default: None
relaxation:
visibility: advanced
dtype: float
description: Float. Relaxation parameter for the update step. A higher value can improve the convergence rate, but one runs the risk of instabilities. Values close to or higher than 1 are not recommended.
default: "0.15"
Key
visibility: The visibility level of the parameter
dtype: The type of data
description: A short explanation of the parameter
default: The default value
options: A list of permitted values
dependency: The name of the parameter and value which this parameter depends upon
range: A guide for the range of the parameter
Citations
Principles of computerized tomographic imaging by Kak, Avinash C et al.
Bibtex
@article{kak2002principles,
title={Principles of computerized tomographic imaging},
author={Kak, Avinash C and Slaney, Malcolm and Wang, Ge},
journal={Medical Physics},
volume={29},
number={1},
pages={107--107},
year={2002},
publisher={Wiley Online Library}
}
Endnote
%0 Journal Article
%T Principles of computerized tomographic imaging
%A Kak, Avinash C
%A Slaney, Malcolm
%A Wang, Ge
%J Medical Physics
%V 29
%N 1
%P 107-107
%@ 0094-2405
%D 2002
%I Wiley Online Library
API
-
class
ScikitimageSart
[source] -
get_max_frames
()[source] Number of data frames to pass to each instance of process_frames func
- Returns
- “single”, “multiple” or integer (only to be used if the number of
frames MUST be fixed.)
- Return type
str or int
-
process_frames
(data)[source] This method is called after the plugin has been created by the pipeline framework and forms the main processing step
- Parameters
data (list(np.array)) – A list of numpy arrays for each input dataset.
-