rmlseg package

Submodules

rmlseg.rmlseg module

Main module, where all the main functions can be found.

@author: Henri DER SARKISSIAN, Nicola VIGANÒ

rmlseg.rmlseg.denoise(img, iterations=50, lambda_tv=0.01, lambda_smooth=0.01, img_max=255.0, data_type=<class 'numpy.float32'>)[source]

This function denoises the input image, based on the given TV and smoothnes constraint weights.

It returns a denoised image.

Parameters

  • img – The image (np.array_like)

  • iterations – Number of iterations (int)

  • lambda_tv – Weight of the TV regularization (float, default: 1e-2)

  • lambda_smooth – Weight of the smoothing regularization (float, default: 1e-2)

  • img_max – renormalization value (float, default: 255.0)

  • data_type – Expected data type (np.dtype, default: np.float32)

Returns

a denoised image

Return type

np.array_like

rmlseg.rmlseg.estimate_rhos(p, projs, img, rhos0=None, iterations=100, dataterm_norm_p=1)[source]

This function estimates the levelset values from the current segmentation and the available projections.

It returns the etimated rhos.

Parameters

  • p – Projector from tomo module

  • projs – The object projections (np.array_like)

  • img – The segmented image (np.array_like)

  • rhos0 – Initial estimation of the rhos (np.array_like, default: None)

  • iterations – Number of iterations (int)

  • dataterm_norm_p – l_p norm of the data term (int, default: 1)

Returns

estimated rhos

Return type

np.array_like

rmlseg.rmlseg.refine_rre(img, rhos, local_rre, iterations=50, lambda_tv=1.0, weight_norm_p=1, dataterm_norm_p=1, data_type=<class 'numpy.float32'>)[source]

This function computes the refinement of the segmented image, based on the given locally reconstructed residual.

It returns the refined image.

Parameters

  • img – The image (np.array_like)

  • rhos – Expected levels (np.array_like)

  • local_rre – Locally reconstructed residual error (np.array_like)

  • iterations – Number of iterations (int)

  • lambda_tv – Weight of the TV regularization (float, default: 1.0)

  • weight_norm_p – l_p norm of the weights (int, default: 1)

  • dataterm_norm_p – l_p norm of the data term (int, default: 1)

  • data_type – Expected data type (np.dtype, default: np.float32)

Returns

a refined regularized image

Return type

np.array_like

rmlseg.rmlseg.regularize_levelsets(img, rhos, iterations=50, lambda_tv=0.1, lambda_smooth=None, weight_norm_p=2, dataterm_norm_p=1, lower_limit=None, upper_limit=None, data_type=<class 'numpy.float32'>)[source]

This function computes the regularization of the input image, based on the given expected level values and regularization weights.

It returns the regularized image.

Parameters

  • img – The image (np.array_like)

  • rhos – Expected levels (np.array_like)

  • iterations – Number of iterations (int)

  • lambda_tv – Weight of the TV regularization (float, default: 1e-1)

  • lambda_smooth – Weight of the smoothing regularization (float, default: None)

  • weight_norm_p – l_p norm of the weights (int, default: 2)

  • dataterm_norm_p – l_p norm of the data term (int, default: 1)

  • lower_limit – Lower limit of the image, used for clipping (float, default: None)

  • upper_limit – Upper limit of the image, used for clipping (float, default: None)

  • data_type – Expected data type (np.dtype, default: np.float32)

Returns

a regularized image

Return type

np.array_like

rmlseg.rmlseg.segment_levelset(img, rhos, *args, **kwds)[source]

This function computes the simple segmentation of the input image, based on the given expected level values and regularization weights.

It returns the segmented image.

Parameters

  • img – The image (np.array_like)

  • rhos – Expected levels (np.array_like)

  • iterations – Number of iterations (int)

  • lambda_tv – Weight of the TV regularization (float, default: 1e-2)

  • lambda_smooth – Weight of the smoothing regularization (float, default: None)

  • weight_norm_p – l_p norm of the weights (int, default: 2)

  • dataterm_norm_p – l_p norm of the data term (int, default: 1)

  • lower_limit – Lower limit of the image, used for clipping (float, default: None)

  • upper_limit – Upper limit of the image, used for clipping (float, default: None)

Returns

a segmented image

Return type

np.array_like

rmlseg.rmlseg.segment_simple(img, rhos)[source]

This function computes the simple segmentation of the input image, based on the given expected level values.

It returns the regularized image.

Parameters

  • img – The image (np.array_like)

  • rhos – Expected levels (np.array_like)

Returns

The segmented image

Return type

np.array_like

rmlseg.tomo module

Basic tomography operations, including a projector class.

@author: Nicola VIGANÒ

class rmlseg.tomo.Projector2D(vol_shape, angles, beam_shape='parallel')[source]

Bases: object

bp(projections)[source]
cgls(projections, iterations=50, opts={})[source]
dispose_projector()[source]
fbp(projections, iterations=50, opts={})[source]
fp(volume)[source]
get_matrix()[source]
initialize_projector()[source]
sirt(projections, iterations=50, opts={})[source]

rmlseg.util module

Utility functions for the use of the package functionality.

@author: Nicola VIGANÒ

rmlseg.util.estimate_local_rre(seg_vol, rhos, projs, angles)[source]

This function estimates the local Reconstructed Residual Error (RRE) for the given segmentation.

Parameters

  • seg_vol – The segmentation (np.array_like)

  • rhos – The segmentation target levels (np.array_like)

  • projs – The projection data (np.array_like)

  • angles – Corresponding angles of the projections (np.array_like)

Returns

The corresponding RRE

Return type

np.array_like

rmlseg.util.reconstruct_simple_2D(rec_vol_shape, projs, angles)[source]

This function computes a simple FBP reconstruction of the projections from the given projection data and corresponding angles, for the given recontruction shape.

Parameters

  • rec_vol_shape – The output reconstruction shape (np.array_like)

  • projs – The projection data (np.array_like)

  • angles – Corresponding angles of the projections (np.array_like)

Returns

The reconstructed image

Return type

np.array_like

rmlseg.util.refine_rre(seg_vol, rhos, rre)[source]

This function refines the segmentation using the RRE.

Parameters

  • seg_vol – The segmentation (np.array_like)

  • rhos – The segmentation target levels (np.array_like)

  • rre – The pixel-wise RRE (np.array_like)

Returns

The segmented image

Return type

np.array_like

rmlseg.util.segment_denoise(rec_vol, rhos)[source]

This function computes the segmentation of the denoised image.

Parameters

  • rec_vol – The reconstruction (np.array_like)

  • rhos – The segmentation target levels (np.array_like)

Returns

The segmented image

Return type

np.array_like

rmlseg.util.segment_levelset(rec_vol, rhos)[source]

This function computes the segmentation of the relaxed levelset based regularization.

Parameters

  • rec_vol – The reconstruction (np.array_like)

  • rhos – The segmentation target levels (np.array_like)

Returns

The segmented image

Return type

np.array_like

Module contents

Top-level package for PyRMLSeg.