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spatial_resolution.py
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spatial_resolution.py
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"""
Spatial Resolution
Contributors:
Haris Shuaib, [email protected]
Neil Heraghty, [email protected], 16/05/2018
.. todo::
Replace shape finding functions with hazenlib.utils equivalents
"""
import os
import sys
import copy
import traceback
import cv2 as cv
import numpy as np
from numpy.fft import fftfreq
import hazenlib.utils
from hazenlib.HazenTask import HazenTask
from hazenlib.logger import logger
class SpatialResolution(HazenTask):
"""Spatial resolution measurement class for DICOM images of the MagNet phantom
Inherits from HazenTask class
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.single_dcm = self.dcm_list[0]
def run(self) -> dict:
"""Main function for performing spatial resolution measurement
Returns:
dict: results are returned in a standardised dictionary structurespecifying the task name, input DICOM Series Description + SeriesNumber + InstanceNumber, task measurement key-value pairs, optionally path to the generated images for visualisation
"""
results = self.init_result_dict()
results["file"] = self.img_desc(self.single_dcm)
try:
pe_result, fe_result = self.calculate_mtf(self.single_dcm)
results["measurement"] = {
"phase encoding direction mm": round(pe_result, 2),
"frequency encoding direction mm": round(fe_result, 2),
}
except Exception as e:
print(
f"Could not calculate the spatial resolution for {self.img_desc(self.single_dcm)} because of : {e}"
)
traceback.print_exc(file=sys.stdout)
# only return reports if requested
if self.report:
results["report_image"] = self.report_files
return results
def get_circles(self, image):
"""Locate Hough Circles in a DICOM pixel array
Args:
image (array): DICOM pixel array rescaled to byte
Returns:
np.array: pixel array of the located circle
"""
# TODO: use shape functions from utils
v = np.median(image)
upper = int(min(255, (1.0 + 5) * v))
i = 40
while True:
circles = cv.HoughCircles(
image,
cv.HOUGH_GRADIENT,
1.2,
256,
param1=upper,
param2=i,
minRadius=80,
maxRadius=200,
)
# min and max radius need to accomodate at least 256 and 512 matrix sizes
i -= 1
if circles is None:
pass
else:
circles = np.uint16(np.around(circles))
break
# img = cv.circle(image, (circles[0][0][0], circles[0][0][1]), circles[0][0][2], (255, 0, 0))
# plt.imshow(img)
# plt.show()
return circles
def thresh_image(self, img, bound=150):
"""Create a threshold image
Args:
img (np.array): pixel array
bound (int, optional): _description_. Defaults to 150.
Returns:
np.array: thresholded pixel array
"""
blurred = cv.GaussianBlur(img, (5, 5), 0)
thresh = cv.threshold(blurred, bound, 255, cv.THRESH_TOZERO_INV)[1]
return thresh
def find_square(self, img):
# TODO: use shape functions from utils
cnts = cv.findContours(img.copy(), cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)[0]
for c in cnts:
perimeter = cv.arcLength(c, True)
approx = cv.approxPolyDP(c, 0.1 * perimeter, True)
if len(approx) == 4:
# compute the bounding box of the contour and use the
# bounding box to compute the aspect ratio
rect = cv.minAreaRect(approx)
# OpenCV 4.5 adjustment
# - cv.minAreaRect() output tuple order changed since v3.4
# - swap rect[1] order & rotate rect[2] by -90
# – convert tuple>list>tuple to do this
rectAsList = list(rect)
rectAsList[1] = (rectAsList[1][1], rectAsList[1][0])
rectAsList[2] = rectAsList[2] - 90
rect = tuple(rectAsList)
box = cv.boxPoints(rect)
box = np.int0(box)
w, h = rect[1]
ar = w / float(h)
# make sure that the width of the square is reasonable size taking into account 256 and 512 matrix
if not 20 < w < 100:
continue
# a square will have an aspect ratio that is approximately
# equal to one, otherwise, the shape is a rectangle
if 0.92 < ar < 1.08:
break
# points should start at top-right and go anti-clockwise
top_corners = sorted(box, key=lambda x: x[1])[:2]
top_corners = sorted(top_corners, key=lambda x: x[0], reverse=True)
bottom_corners = sorted(box, key=lambda x: x[1])[2:]
bottom_corners = sorted(bottom_corners, key=lambda x: x[0])
return top_corners + bottom_corners, box
def get_roi(self, pixels, centre, size=20):
"""Get coordinates of the region of interest
Args:
pixels (np.array): _description_
centre (tuple): x,y (int) coordinates
size (int, optional): diameter of the region of interest. Defaults to 20.
Returns:
np.array: subset of the pixel array
"""
y, x = centre
arr = pixels[x - size // 2 : x + size // 2, y - size // 2 : y + size // 2]
return arr
def get_void_roi(self, pixels, circle, size=20):
"""Create an 'empty' region of interest - same size filles with 0
Args:
pixels (np.array): _description_
centre (tuple): x,y (int) coordinates
size (int, optional): diameter of the region of interest. Defaults to 20.
Returns:
np.array: subset of the pixel array
"""
centre_x = circle[0][0][0]
centre_y = circle[0][0][1]
return self.get_roi(pixels=pixels, centre=(centre_x, centre_y), size=size)
def get_edge_roi(self, pixels, edge_centre, size=20):
return self.get_roi(
pixels, centre=(edge_centre["x"], edge_centre["y"]), size=size
)
return self.get_roi(
pixels, centre=(edge_centre["x"], edge_centre["y"]), size=size
)
def edge_is_vertical(self, edge_roi, mean) -> bool:
"""Determine whether edge is vertical
control_parameter_01=0 ;a control parameter that will be equal to 1 if the edge is vertical and 0 if it is horizontal
for column=0, event.MTF_roi_size-2 do begin
if MTF_Data(column, 0 ) EQ mean_value then control_parameter_01=1
if (MTF_Data(column, 0) LT mean_value) AND (MTF_Data(column+1, 0) GT mean_value) then control_parameter_01=1
if (MTF_Data(column, 0) GT mean_value) AND (MTF_Data(column+1, 0) LT mean_value) then control_parameter_01=1
end
Args:
edge_roi (np.array): pixel array in ROI
mean (np.array): array of mean pixel values
Returns:
bool: True or false whether edge is vertical
"""
for col in range(edge_roi.shape[0] - 1):
if edge_roi[col, 0] == mean:
return True
if edge_roi[col, 0] < mean < edge_roi[col + 1, 0]:
return True
if edge_roi[col, 0] > mean > edge_roi[col + 1, 0]:
return True
return False
def get_bisecting_normal(self, vector, centre, length_factor=0.25):
# calculate coordinates of bisecting normal
nrx_1 = centre["x"] - int(length_factor * vector["y"])
nry_1 = centre["y"] + int(length_factor * vector["x"])
nrx_2 = centre["x"] + int(length_factor * vector["y"])
nry_2 = centre["y"] - int(length_factor * vector["x"])
return nrx_1, nry_1, nrx_2, nry_2
def get_top_edge_vector_and_centre(self, square):
# Calculate dx and dy
top_edge_profile_vector = {
"x": (square[0][0] + square[1][0]) // 2,
"y": (square[0][1] + square[1][1]) // 2,
}
# Calculate centre (x,y) of edge
top_edge_profile_roi_centre = {
"x": (square[0][0] + square[1][0]) // 2,
"y": (square[0][1] + square[1][1]) // 2,
}
return top_edge_profile_vector, top_edge_profile_roi_centre
def get_right_edge_vector_and_centre(self, square):
# Calculate dx and dy
right_edge_profile_vector = {
"x": square[3][0] - square[0][0],
"y": square[3][1] - square[0][1],
} # nonsense
# Calculate centre (x,y) of edge
right_edge_profile_roi_centre = {
"x": (square[3][0] + square[0][0]) // 2,
"y": (square[3][1] + square[0][1]) // 2,
}
return right_edge_profile_vector, right_edge_profile_roi_centre
def get_signal_roi(self, pixels, edge, edge_centre, circle_r, size=20):
"""Get pixel array from the image within ROI
Args:
pixels (np.array): _description_
edge (_type_): _description_
edge_centre (_type_): _description_
circle_r (float/int): circle radius
size (int, optional): diameter of the region of interest. Defaults to 20.
Returns:
np.array: subset of the pixel array
"""
if edge == "right":
x = edge_centre["x"] + circle_r // 2
y = edge_centre["y"]
elif edge == "top":
x = edge_centre["x"]
y = edge_centre["y"] - circle_r // 2
return self.get_roi(pixels=pixels, centre=(x, y), size=size)
def get_edge(self, edge_arr, mean_value, spacing):
# TODO: simplify this function
if self.edge_is_vertical(edge_arr, mean_value):
edge_arr = np.rot90(edge_arr)
x_edge = [0] * 20
y_edge = [0] * 20
for row in range(20):
for col in range(19):
control_parameter_02 = 0
if edge_arr[row, col] == mean_value:
control_parameter_02 = 1
if (edge_arr[row, col] < mean_value) and (
edge_arr[row, col + 1] > mean_value
):
control_parameter_02 = 1
if (edge_arr[row, col] > mean_value) and (
edge_arr[row, col + 1] < mean_value
):
control_parameter_02 = 1
if control_parameter_02 == 1:
x_edge[row] = row * spacing[0]
y_edge[row] = col * spacing[1]
return x_edge, y_edge, edge_arr
def get_edge_angle_and_intercept(self, x_edge, y_edge):
"""Get edge (slope) angle and intercept value
Args:
x_edge (np.ndarray): _description_
y_edge (np.ndarray): _description_
Returns:
tuple: angle and intercept values
"""
# ;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# ;Apply least squares method for the edge
# ;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
mean_x = np.mean(x_edge)
mean_y = np.mean(y_edge)
slope_up = np.sum((x_edge - mean_x) * (y_edge - mean_y))
slope_down = np.sum((x_edge - mean_x) * (x_edge - mean_x))
slope = slope_up / slope_down
angle = np.arctan(slope)
intercept = mean_y - slope * mean_x
return angle, intercept
def get_edge_profile_coords(self, angle, intercept, spacing):
"""translate and rotate the data's coordinates according to the slope and intercept
Args:
angle (ndarray or scalar): angle of slope
intercept (ndarray or scalar): intercept of slope
spacing (tuple/list): spacing value in x and y directions
Returns:
tuple: of np.ndarrays of the rotated MTF positions in x and y directions
"""
original_mtf_x_position = np.array([x * spacing[0] for x in range(20)])
original_mtf_x_positions = copy.copy(original_mtf_x_position)
for row in range(19):
original_mtf_x_positions = np.row_stack(
(original_mtf_x_positions, original_mtf_x_position)
)
original_mtf_y_position = np.array([x * spacing[1] for x in range(20)])
original_mtf_y_positions = copy.copy(original_mtf_y_position)
for row in range(19):
original_mtf_y_positions = np.column_stack(
(original_mtf_y_positions, original_mtf_y_position)
)
# we are only interested in the rotated y positions as there correspond to the distance of the data from the edge
rotated_mtf_y_positions = -original_mtf_x_positions * np.sin(angle) + (
original_mtf_y_positions - intercept
) * np.cos(angle)
rotated_mtf_x_positions = original_mtf_x_positions * np.cos(angle) + (
original_mtf_y_positions - intercept
) * np.sin(angle)
return rotated_mtf_x_positions, rotated_mtf_y_positions
def get_esf(self, edge_arr, y):
"""Extract the edge response function
Args:
edge_arr (np.ndarray): _description_
y (np.ndarray): _description_
Returns:
tuple: u and esf - 'normal' and interpolated edge response function (ESF)
"""
# extract the distance from the edge and the corresponding data as vectors
edge_distance = copy.copy(y[0, :])
for row in range(1, 20):
edge_distance = np.append(edge_distance, y[row, :])
esf_data = copy.copy(edge_arr[:, 0])
for row in range(1, 20):
esf_data = np.append(esf_data, edge_arr[:, row])
# sort the distances and the data accordingly
ind_edge_distance = np.argsort(edge_distance)
sorted_edge_distance = edge_distance[ind_edge_distance]
sorted_esf_data = esf_data[ind_edge_distance]
# get rid of duplicates (if two data correspond to the same distance) and replace them with their average
temp_array01 = np.array([sorted_edge_distance[0]])
temp_array02 = np.array([sorted_esf_data[0]])
for element in range(1, len(sorted_edge_distance)):
if not (sorted_edge_distance[element] - temp_array01[-1]).all():
temp_array02[-1] = (temp_array02[-1] + sorted_esf_data[element]) / 2
else:
temp_array01 = np.append(temp_array01, sorted_edge_distance[element])
temp_array02 = np.append(temp_array02, sorted_esf_data[element])
# ;interpolate the edge response function (ESF) so that it only has 128 elements
u = np.linspace(temp_array01[0], temp_array01[-1], 128)
esf = np.interp(u, temp_array01, temp_array02)
return u, esf
def calculate_mtf_for_edge(self, dicom, edge):
pixels = dicom.pixel_array
pe = dicom.InPlanePhaseEncodingDirection
img = hazenlib.utils.rescale_to_byte(pixels) # rescale for OpenCV operations
thresh = self.thresh_image(img)
circle = self.get_circles(img)
circle_radius = circle[0][0][2]
square, box = self.find_square(thresh)
if edge == "right":
_, centre = self.get_right_edge_vector_and_centre(square)
else:
_, centre = self.get_top_edge_vector_and_centre(square)
edge_arr = self.get_edge_roi(pixels, centre)
void_arr = self.get_void_roi(pixels, circle)
signal_arr = self.get_signal_roi(pixels, edge, centre, circle_radius)
spacing = hazenlib.utils.get_pixel_size(dicom)
mean = np.mean([void_arr, signal_arr])
x_edge, y_edge, edge_arr = self.get_edge(edge_arr, mean, spacing)
angle, intercept = self.get_edge_angle_and_intercept(x_edge, y_edge)
x, y = self.get_edge_profile_coords(angle, intercept, spacing)
u, esf = self.get_esf(edge_arr, y)
# This function calculated the LSF by taking the derivative of the ESF.
# Reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3643984/
lsf = np.gradient(esf)
lsf = np.array(lsf)
n = lsf.size
mtf = abs(np.fft.fft(lsf))
norm_mtf = mtf / mtf[0]
mtf_50 = min(
[
i
for i in range(len(norm_mtf) - 1)
if norm_mtf[i] >= 0.5 >= norm_mtf[i + 1]
]
)
profile_length = max(y.flatten()) - min(y.flatten())
freqs = fftfreq(n, profile_length / n)
mask = freqs >= 0
mtf_frequency = 10.0 * mtf_50 / profile_length
res = 10 / (2 * mtf_frequency)
if self.report:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(11, 1)
fig.set_size_inches(5, 36)
fig.tight_layout(pad=4)
axes[0].set_title("raw pixels")
axes[0].imshow(pixels, cmap="gray")
axes[1].set_title("rescaled to byte")
axes[1].imshow(img, cmap="gray")
axes[2].set_title("thresholded")
axes[2].imshow(thresh, cmap="gray")
axes[3].set_title("finding circle")
c = cv.circle(
img, (circle[0][0][0], circle[0][0][1]), circle[0][0][2], (255, 0, 0)
)
axes[3].imshow(c)
box = cv.drawContours(img, [box], 0, (255, 0, 0), 1)
axes[4].set_title("finding MTF square")
axes[4].imshow(box)
axes[5].set_title("edge ROI")
axes[5].imshow(edge_arr, cmap="gray")
axes[6].set_title("void ROI")
im = axes[6].imshow(void_arr, cmap="gray")
fig.colorbar(im, ax=axes[6])
axes[7].set_title("signal ROI")
im = axes[7].imshow(signal_arr, cmap="gray")
fig.colorbar(im, ax=axes[7])
axes[8].set_title("edge spread function")
axes[8].plot(esf)
axes[8].set_xlabel("mm")
axes[9].set_title("line spread function")
axes[9].plot(lsf)
axes[9].set_xlabel("mm")
axes[10].set_title("normalised MTF")
axes[10].plot(freqs[mask], norm_mtf[mask])
axes[10].set_xlabel("lp/mm")
logger.debug(f"Writing report image: {self.report_path}_{pe}_{edge}.png")
img_path = os.path.realpath(
os.path.join(
self.report_path, f"{self.img_desc(dicom)}_{pe}_{edge}.png"
)
)
fig.savefig(img_path)
self.report_files.append(img_path)
return res
def calculate_mtf(self, dicom) -> tuple:
pe = dicom.InPlanePhaseEncodingDirection
pe_result, fe_result = None, None
if pe == "COL":
pe_result = self.calculate_mtf_for_edge(dicom, "top")
fe_result = self.calculate_mtf_for_edge(dicom, "right")
elif pe == "ROW":
pe_result = self.calculate_mtf_for_edge(dicom, "right")
fe_result = self.calculate_mtf_for_edge(dicom, "top")
return pe_result, fe_result