src.imagedata.apps.Siemens package

Submodules

src.imagedata.apps.Siemens.ROI module

ROI objects

class src.imagedata.apps.Siemens.ROI.EllipseROI(centre, angles, thickness, label, stu_ins_uid, ser_ins_uid, sop_ins_uid)

Bases: ROI

Ellipse ROI object.

Variables:

• centre_cm

• angles_cm

• thickness_cm

• radius_cm

• radius_matrix

draw_roi_on_canvas(canvas, colour=1, threshold=0.5, fill=False)

Make a 2D mask [ny,nx] on canvas

Parameters:

• canvas – 2D [ny,nx]

• fill – fill color

• self.points_matrix – polygon points

• self._slice

Returns:

canvas

get_points_cm()

Get ROI points as Numpy array in cm real coordinates

Returns:

np.array((points,3)) where each row is (z,y,x) in cm

get_points_matrix(si)

Get ROI points as Numpy array in matrix coordinates

Parameters:

si – Series with transformation matrix

Returns:

np.array((points,3)) where each row is (z,y,x) in matrix coordinates

class src.imagedata.apps.Siemens.ROI.PolygonROI(polygon, label, stu_ins_uid, ser_ins_uid, sop_ins_uid)

Bases: ROI

Polygon ROI object.

Variables:

points

draw_roi_on_canvas(canvas, colour=1, threshold=0.5, fill=False)

Make a 2D mask [ny,nx] on canvas

Parameters:

• canvas – 2D [ny,nx]

• self.points_matrix – polygon points

• self._slice

• fill – whether to fill polygon interior

Returns:

canvas

get_points_cm()

Get ROI points as Numpy array in cm real coordinates

Returns:

np.array((points,3)) where each row is (z,y,x) in cm

get_points_matrix(si)

Get ROI points as Numpy array in matrix coordinates

Parameters:

si – Series with transformation matrix

Returns:

np.array((points,3)) where each row is (z,y,x) in matrix coordinates

class src.imagedata.apps.Siemens.ROI.ROI(label, stu_ins_uid, ser_ins_uid, sop_ins_uid)

Bases: object

General ROI object.

Variables:

• points

• points_matrix

• slice

• label

• stu_ins_uid

• ser_ins_uid

• sop_ins_uid

static create_canvas(shape, mode=<class 'bool'>)

Make a 2D [ny,nx] canvas

Parameters:

• shape – 4D [nt,nz,ny,nx] or 3D [nz,ny,nx] shape

• mode – bool for binary image, np.uint8 for 8-bit grayscale image

abstract draw_roi_on_canvas(canvas, colour=1, threshold=0.5, fill=False)

Make a 2D mask [ny,nx] on canvas

Parameters:

• canvas – 2D [ny,nx]

• colour – mask color

• fill – whether to fill interior of ROI

• self.points_matrix – polygon points

• self._slice

Returns:

binary img of shape [ny,nx]

draw_roi_on_numpy(mask, colour=1, threshold=0.5, fill=True)

Draw the ROI on an existing numpy array

Parameters:

• mask – numpy array [nz,ny,nx]

• colour – mask colour (int)

• threshold – alpha blend (float)

• fill – whether to fill interior of ROI (bool)

Returns:

modified numpy array [nz,ny,nx]

Return type:

mask

abstract get_points_cm()

Get ROI points as Numpy array in cm real coordinates

Returns:

np.array([points,3]) where each row is (z,y,x) in cm

abstract get_points_matrix(si)

Get ROI points as Numpy array in matrix coordinates

Parameters:

si – Series with transformation matrix

Returns:

np.array([points,3]) where each row is (z,y,x) in matrix coordinates

get_timepoint(si)

static transform_data_points_to_voxels(si, points)

verify_all_voxels_in_slice()

src.imagedata.apps.Siemens.draw_antialiased module

Library to draw an antialiased line. http://stackoverflow.com/questions/3122049/drawing-an-anti-aliased-line-with-thepython-imaging-library https://en.wikipedia.org/wiki/Xiaolin_Wu%27s_line_algorithm

https://yellowsplash.wordpress.com/2009/10/23/fast-antialiased-circles-and-ellipses-from-xiaolin-wus-concepts/ https://stackoverflow.com/questions/37589165/drawing-an-antialiased-circle-as-described-by-xaolin-wu#37714284

src.imagedata.apps.Siemens.draw_antialiased.boundary_fill4(canvas, start_x, start_y, boundary_value, fill_value)

src.imagedata.apps.Siemens.draw_antialiased.draw_circle_mask(canvas, center_x, center_y, outer_radius, colour, threshold, fill=True)

Draw circle mask on NumPy array.

Apply algorithm for drawing anti-aliased circle. Draw points only when the alpha blending is above a set threshold. Only given _colour value is drawn. Intended usage is as a mask index.

Optionally, fill the circle with the same _colour.

Reference:

https://stackoverflow.com/questions/37589165/drawing-an-antialiased-circle-as-described-by-xaolin-wu#37714284

Parameters:

• canvas – numpy array

• center_x – center of circle in array coordinates (int)

• center_y – center of circle in array coordinates (int)

• outer_radius – radius of circle in array dimension (float)

• colour – _colour to draw (mask index) (int)

• threshold – only pixels with an alpha > threshold will be drawn (float, 0.0-1.0)

• fill – whether to fill the circle (boolean)

src.imagedata.apps.Siemens.draw_antialiased.draw_ellipse_mask(canvas, center_x, center_y, outer_radius, colour, threshold, fill=True)

Draw ellipse mask on NumPy array.

Apply algorithm for drawing anti-aliased ellipse. Draw points only when the alpha blending is above a set threshold. Only given _colour value is drawn. Intended usage is as a mask index.

Optionally, fill the ellipse with the same _colour.

Reference:

https://yellowsplash.wordpress.com/2009/10/23/fast-antialiased-circles-and-ellipses-from-xiaolin-wus-concepts/ https://stackoverflow.com/questions/37589165/drawing-an-antialiased-circle-as-described-by-xaolin-wu#37714284

Parameters:

• canvas – numpy array

• center_x – center of ellipse in array coordinates (int)

• center_y – center of ellipse in array coordinates (int)

• outer_radius – radius of circle in array dimension (float)

• colour – colour to draw (mask index) (int)

• threshold – only pixels with an alpha > threshold will be drawn (float, 0.0-1.0)

• fill – whether to fill the circle (boolean)

src.imagedata.apps.Siemens.draw_antialiased.draw_line_mask(canvas, x1, y1, x2, y2, colour, threshold)

Draw line mask on NumPy array.

Apply the Xialon Wu anti-aliasing algorithm for drawing line. Draw points only when the alpha blending is above a set threshold. Only given colour value is drawn. Intended usage is as a mask index.

Parameters:

• canvas – 2D numpy array (np.ndarray)

• x1 (float) – line end points

• y1 (float) – line end points

• x2 (float) – line end points

• y2 (float) – line end points

• colour (int) – colour to draw ‘mask index’

• threshold (float) – only pixels with an alpha > threshold will be drawn, 0.0-1.0

src.imagedata.apps.Siemens.draw_antialiased.draw_polygon_mask(canvas, points, colour, threshold, fill=True)

Make a 2D mask [ny,nx] on canvas

Optionally, fill the polygon with the same colour.

Parameters:

• canvas – numpy array

• points – polygon points (list of tuples (x,y)) (float)

• colour – colour to draw (mask index) (int)

• threshold – only pixels with an alpha > threshold will be drawn (float, 0.0-1.0)

• fill – whether to fill the circle (boolean)

src.imagedata.apps.Siemens.draw_antialiased.flood_fill4(canvas, start_x, start_y, old_value, fill_value)

src.imagedata.apps.Siemens.draw_antialiased.fpart(x)

Returns the fractional part of x.

src.imagedata.apps.Siemens.draw_antialiased.ipart(x)

Floors x.

src.imagedata.apps.Siemens.draw_antialiased.iround(x)

Rounds x to the nearest integer.

src.imagedata.apps.Siemens.draw_antialiased.plot(canvas, x, y, steep, colour, alpha, threshold)

Draws an anti-aliased pixel on a line.

src.imagedata.apps.Siemens.draw_antialiased.point_in_polygon(canvas, polygon)

src.imagedata.apps.Siemens.draw_antialiased.rfpart(x)

Returns the 1 minus the fractional part of x.

src.imagedata.apps.Siemens.evidence2mask module

Read Siemens Syngo.VIA Basic Reading

src.imagedata.apps.Siemens.evidence2mask.evidence2roi(im, uid_table=None, content=None)

overrows = image[(0x6000, 0x0010)].value overcols = image[(0x6000, 0x0011)].value overkey=(0x6000, 0x3000) overlay=image[overkey].value

a=bitarray.bitarray(overlay, endian=’little’)

while len(a) < len(overlay)*8:

a.append(0)

c = np.array(a.tolist()) c = c.astype(np.uint8) #c = c.reshape((overcols, overrows)) c = c.reshape((overrows, overcols))

src.imagedata.apps.Siemens.evidence2mask.make_mask_in_slice(roi_type, si, points, shape)

Make a 3D mask [nz,ny,nx]

src.imagedata.apps.Siemens.evidence2mask.transform_data_points_to_voxels(si, meas_data_points)

src.imagedata.apps.Siemens.evidence2mask.xyz_to_zyx(polygon)

Swap (x,y,z) with (z,y,x) for each point

Parameters:

polygon – np.array shape (n,3)

Returns:

rearranged np.array shape (n,3)

src.imagedata.apps.Siemens.evidence_main module

Module contents