ultrasound_metrics.metrics.utils#

Utility functions for ultrasound metrics calculations.

Helper functions for dimension handling, indexing, and interpolation used by various metrics.

Functions#

`calculate_linear_interpolation`(→ float)	Calculate linear interpolation between two points.
`find_max_idx_within_expected_target_radius`(...)	Find maximum index within expected target radius.
`get_subscript_idx_of_target_max_within_roi`(...)	Get subscript index of maximum value within ROI.
`reduce_image_to_1D_at_point`(...)	Reduce image to 1D at specified point.
`reduce_image_to_only_measurement_dimensions`(...)	Reduce image to only specified measurement dimensions.
`reduce_max_idx_to_only_measurement_dimensions`(...)	Reduce maximum index to only measurement dimensions.
`reorder_measured_dims_into_original_shape`(...)	Reorder measured dimensions into original image shape.
`set_default_measurement_dims`(...)	Set default measurement dimensions based on image shape.

Module Contents#

ultrasound_metrics.metrics.utils.calculate_linear_interpolation( a: float, b: float, a_idx: int, b_idx: int, interp_val: float, ) → float[source]#

Calculate linear interpolation between two points.

Parameters:

a – Value at first point.
b – Value at second point.
a_idx – Index of first point.
b_idx – Index of second point.
interp_val – Value to interpolate to.

Returns:

Interpolated index value.

Return type:

float

ultrasound_metrics.metrics.utils.find_max_idx_within_expected_target_radius( img: numpy.typing.NDArray[numpy.floating], img_max_idx: tuple[int, Ellipsis], radius: int, ) → tuple[int, Ellipsis][source]#

Find maximum index within expected target radius.

Parameters:

img – Input image array.
img_max_idx – Initial maximum index.
radius – Search radius around the initial maximum.

Returns:

Index of maximum value within the specified radius.

Return type:

tuple

ultrasound_metrics.metrics.utils.get_subscript_idx_of_target_max_within_roi( img: numpy.typing.NDArray[numpy.floating], roi_indices: numpy.typing.NDArray[numpy.integer], ) → tuple[int, Ellipsis][source]#

Get subscript index of maximum value within ROI.

Parameters:

img – Input image array.
roi_indices – Indices defining the region of interest.

Returns:

Subscript index of the maximum value within the ROI.

Return type:

tuple

ultrasound_metrics.metrics.utils.reduce_image_to_1D_at_point( img: numpy.typing.NDArray[numpy.floating], point: tuple[int, Ellipsis], keep_dim: int, ) → numpy.typing.NDArray[numpy.floating][source]#

Reduce image to 1D at specified point.

Parameters:

img – Input image array.
point – Point coordinates to extract.
keep_dim – Dimension to keep (others will be sliced at the point).

Returns:

1D array extracted from the image at the specified point.

Return type:

ndarray

ultrasound_metrics.metrics.utils.reduce_image_to_only_measurement_dimensions( img: numpy.typing.NDArray[numpy.floating], measurement_dimensions: numpy.typing.NDArray[numpy.integer], img_max_idx: tuple[int, Ellipsis], ) → numpy.typing.NDArray[numpy.floating][source]#

Reduce image to only specified measurement dimensions.

Parameters:

img – Input image array.
measurement_dimensions – Dimensions to keep in the output.
img_max_idx – Index of maximum value in the image.

Returns:

Image reduced to measurement dimensions only.

Return type:

ndarray

ultrasound_metrics.metrics.utils.reduce_max_idx_to_only_measurement_dimensions( img_max_idx: numpy.typing.NDArray[numpy.integer], dims_to_measure: numpy.typing.NDArray[numpy.integer], ) → tuple[int, Ellipsis][source]#

Reduce maximum index to only measurement dimensions.

Parameters:

img_max_idx – Index of maximum value in the image.
dims_to_measure – Dimensions to keep.

Returns:

Maximum index reduced to measurement dimensions only.

Return type:

tuple

ultrasound_metrics.metrics.utils.reorder_measured_dims_into_original_shape( sll_of_measured_dims: numpy.typing.NDArray[numpy.floating], n_dim_of_input_image: int, measurement_dimensions: numpy.typing.NDArray[numpy.integer], ) → list[float | None][source]#

Reorder measured dimensions into original image shape.

Parameters:

sll_of_measured_dims – Measured values for each dimension.
n_dim_of_input_image – Number of dimensions in the original image.
measurement_dimensions – Dimensions that were measured.

Returns:

List of measured values reordered to match original image dimensions.

Return type:

list

ultrasound_metrics.metrics.utils.set_default_measurement_dims( img_shape: tuple[int, Ellipsis], ) → numpy.typing.NDArray[numpy.integer][source]#

Set default measurement dimensions based on image shape.

Parameters:: img_shape – Shape of the input image.
Returns:: Array of valid measurement dimensions.
Return type:: ndarray