Vo Centering¶
Description¶
A plugin to calculate the centre of rotation using the Vo method
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: The default names
default: "['cor_preview', 'cor_broadcast']"
preview:
visibility: basic
dtype: preview
description: A slice list of required frames (sinograms) to use in the calculation of the centre of rotation (this will not reduce the data size for subsequent plugins).
default: "[]"
example: "The typical three dimensional data structure is [angles, detY, detZ], e.g. for sinogram choose [:,sliceNo,:] [angles, detZ, detY]. If the data is four dimensional, include a time parameter."
start_pixel:
visibility: intermediate
dtype: "[int,None]"
description: The estimated centre of rotation. If value is None, use the horizontal centre of the image.
default: None
search_area:
visibility: basic
dtype: "list[float,float]"
description: Search area around the estimated centre of rotation
default: "[-50, 50]"
ratio:
visibility: intermediate
dtype: float
description: The ratio between the size of a sample and the field of view of a camera
default: "0.5"
search_radius:
visibility: intermediate
dtype: int
description: Use for fine searching
default: "6"
step:
visibility: intermediate
dtype: float
description: Step of fine searching
default: "0.5"
datasets_to_populate:
visibility: intermediate
dtype: "[list[],list[str]]"
description: A list of datasets which require this information
default: "[]"
broadcast_method:
visibility: advanced
dtype: str
options: "['median', 'mean', 'nearest', 'linear_fit']"
description:
summary: Method of broadcasting centre values calculated from preview slices to full dataset.
options:
median: None
mean: None
nearest: None
linear_fit: None
default: median
row_drop:
visibility: advanced
dtype: int
description: Drop lines around vertical center of the mask
default: "20"
average_radius:
visibility: advanced
dtype: int
description: Averaging sinograms around a required sinogram to improve signal-to-noise ratio
default: "5"
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
Reliable method for calculating the center of rotation in parallel-beam tomography by Vo, Nghia T et al.
Bibtex
@article{vo2014reliable,
title={Reliable method for calculating the center of rotation in parallel-beam tomography},
author={Vo, Nghia T and Drakopoulos, Michael and Atwood, Robert C and Reinhard, Christina},
journal={Optics express},
volume={22},
number={16},
pages={19078--19086},
year={2014},
publisher={Optical Society of America}
}
Endnote
%0 Journal Article
%T Reliable method for calculating the center of rotation in parallel-beam tomography
%A Vo, Nghia T
%A Drakopoulos, Michael
%A Atwood, Robert C
%A Reinhard, Christina
%J Optics express
%V 22
%N 16
%P 19078-19086
%@ 1094-4087
%D 2014
%I Optical Society of America
API
-
class
VoCentering
[source] -
executive_summary
()[source] Provide a summary to the user for the result of the plugin.
- e.g.
Warning, the sample may have shifted during data collection
Filter operated normally
- Returns
A list of string summaries
-
fix_transport
()[source]
-
get_max_frames
()[source]
-
nOutput_datasets
()[source] The number of datasets created by the plugin
- Returns
Number of output datasets
-
populate_meta_data
(key, value)[source]
-
post_process
()[source] This method is called after the process function in the pipeline framework as a post-processing step. All processes will have finished performing the main processing at this stage.
- Parameters
exp (experiment class instance) – An experiment object, holding input and output datasets
-
pre_process
()[source] This method is called immediately after base_pre_process().
-
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.
-
set_filter_padding
(in_data, out_data)[source] Should be overridden to define how wide the frame should be for each input data set
-
setup
()[source] This method is first to be called after the plugin has been created. It determines input/output datasets and plugin specific dataset information such as the pattern (e.g. sinogram/projection).
-