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snr.py
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snr.py
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"""
SNR(Im)
Calculates the SNR for a single-slice image of a uniform MRI phantom
This script utilises the smoothed subtraction method described in McCann 2013:
A quick and robust method for measurement of signal-to-noise ratio in MRI, Phys. Med. Biol. 58 (2013) 3775:3790
Created by Neil Heraghty
04/05/2018
"""
import os
import cv2 as cv
import numpy as np
import pydicom
import skimage.filters
from scipy import ndimage
import hazenlib.utils
import hazenlib.exceptions as exc
from hazenlib.HazenTask import HazenTask
from hazenlib.logger import logger
class SNR(HazenTask):
def __init__(self, **kwargs):
super().__init__(**kwargs)
# measured slice width is expected to be a floating point number
try:
self.measured_slice_width = float(kwargs["measured_slice_width"])
except:
self.measured_slice_width = None
def run(self) -> dict:
results = self.init_result_dict()
results['file'] = [self.img_desc(img) for img in self.dcm_list]
results['measurement']["snr by smoothing"] = {}
if len(self.dcm_list) == 2:
snr, normalised_snr = self.snr_by_subtraction(
self.dcm_list[0], self.dcm_list[1], self.measured_slice_width
)
results['measurement']["snr by subtraction"] = {
"measured": round(snr, 2),
"normalised": round(normalised_snr, 2)
}
for idx, dcm in enumerate(self.dcm_list):
snr, normalised_snr = self.snr_by_smoothing(dcm, self.measured_slice_width)
results['measurement']["snr by smoothing"][self.img_desc(dcm)] = {
"measured": round(snr, 2),
"normalised": round(normalised_snr, 2)
}
# only return reports if requested
if self.report:
results['report_image'] = self.report_files
return results
def two_inputs_match(self, dcm1: pydicom.Dataset, dcm2: pydicom.Dataset) -> bool:
"""
Checks if two DICOMs are sufficiently similar
Parameters
----------
dcm1
dcm2
Returns
-------
"""
fields_to_match = ['StudyInstanceUID', 'RepetitionTime', 'EchoTime', 'FlipAngle']
for field in fields_to_match:
if dcm1.get(field) != dcm2.get(field):
return False
return True
def get_normalised_snr_factor(self, dcm: pydicom.Dataset, measured_slice_width=None) -> float:
"""
Calculates SNR normalisation factor. Method matches MATLAB script.
Utilises user provided slice_width if provided. Else finds from dcm.
Finds dx, dy and bandwidth from dcm.
Seeks to find TR, image columns and rows from dcm. Else uses default values.
Parameters
----------
dcm, measured_slice_width
Returns
-------
normalised snr factor: float
"""
dx, dy = hazenlib.utils.get_pixel_size(dcm)
bandwidth = hazenlib.utils.get_bandwidth(dcm)
TR = hazenlib.utils.get_TR(dcm)
rows = hazenlib.utils.get_rows(dcm)
columns = hazenlib.utils.get_columns(dcm)
if measured_slice_width:
slice_thickness = measured_slice_width
else:
slice_thickness = hazenlib.utils.get_slice_thickness(dcm)
averages = hazenlib.utils.get_average(dcm)
bandwidth_factor = np.sqrt((bandwidth * columns / 2) / 1000) / np.sqrt(30)
voxel_factor = (1 / (0.001 * dx * dy * slice_thickness))
normalised_snr_factor = bandwidth_factor * voxel_factor * (1 / (np.sqrt(averages * rows * (TR / 1000))))
return normalised_snr_factor
def filtered_image(self, dcm: pydicom.Dataset) -> np.array:
"""
Performs a 2D convolution (for filtering images)
uses uniform_filter SciPy function
parameters:
---------------
a: array to be filtered
returns:
---------------
filtered numpy array
"""
a = dcm.pixel_array.astype('int')
# filter size = 9, following MATLAB code and McCann 2013 paper for head coil, although note McCann 2013 recommends 25x25 for body coil.
filter_size = 9
# 9 for head coil, 25 for body coil
# TODO make kernel size optional
filtered_array = ndimage.uniform_filter(a, filter_size, mode='constant')
return filtered_array
def get_noise_image(self, dcm: pydicom.Dataset) -> np.array:
"""
Separates the image noise by smoothing the image and subtracting the smoothed image
from the original.
parameters:
---------------
a: image array from dcmread and .pixelarray
returns:
---------------
Imnoise: image representing the image noise
"""
a = dcm.pixel_array.astype('int')
# Convolve image with boxcar/uniform kernel
imsmoothed = self.filtered_image(dcm)
# Subtract smoothed array from original
imnoise = a - imsmoothed
return imnoise
def threshold_image(self, dcm: pydicom.Dataset):
"""
Threshold images
parameters:
---------------
a: image array from dcmread and .pixelarray
returns:
---------------
imthresholded: thresholded image
mask: threshold mask
"""
a = dcm.pixel_array.astype('int')
threshold_value = skimage.filters.threshold_li(a) # threshold_li: Pixels > this value are assumed foreground
# print('threshold_value =', threshold_value)
mask = a > threshold_value
imthresholded = np.zeros_like(a)
imthresholded[mask] = a[mask]
# # For debugging: Threshold figures:
# from matplotlib import pyplot as plt
# plt.figure()
# fig, ax = plt.subplots(2, 2)
# ax[0, 0].imshow(a)
# ax[0, 1].imshow(mask)
# ax[1, 0].imshow(imthresholded)
# ax[1, 1].imshow(a-imthresholded)
# fig.savefig("../THRESHOLD.png")
return imthresholded, mask
def get_binary_mask_centre(self, binary_mask) -> (int, int):
"""
Return centroid coordinates of binary polygonal shape
parameters:
---------------
binary_mask: mask of a shape
returns:
---------------
centroid_coords: (col:int, row:int)
"""
from skimage import util
from skimage.measure import label, regionprops
img = util.img_as_ubyte(binary_mask) > 0
label_img = label(img, connectivity=img.ndim)
props = regionprops(label_img)
col = int(props[0].centroid[0])
row = int(props[0].centroid[1])
# print('Centroid coords [x,y] =', col, row)
return int(col), int(row)
def get_roi_samples(self, ax, dcm: pydicom.Dataset or np.ndarray, centre_col: int, centre_row: int) -> list:
if type(dcm) == np.ndarray:
data = dcm
else:
data = dcm.pixel_array
sample = [None] * 5
# for array indexing: [row, column] format
sample[0] = data[(centre_row - 10):(centre_row + 10), (centre_col - 10):(centre_col + 10)]
sample[1] = data[(centre_row - 50):(centre_row - 30), (centre_col - 50):(centre_col - 30)]
sample[2] = data[(centre_row + 30):(centre_row + 50), (centre_col - 50):(centre_col - 30)]
sample[3] = data[(centre_row - 50):(centre_row - 30), (centre_col + 30):(centre_col + 50)]
sample[4] = data[(centre_row + 30):(centre_row + 50), (centre_col + 30):(centre_col + 50)]
if ax:
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
# for patches: [column/x, row/y] format
rects = [Rectangle((centre_col - 10, centre_row - 10), 20, 20),
Rectangle((centre_col - 50, centre_row - 50), 20, 20),
Rectangle((centre_col + 30, centre_row - 50), 20, 20),
Rectangle((centre_col - 50, centre_row + 30), 20, 20),
Rectangle((centre_col + 30, centre_row + 30), 20, 20)]
pc = PatchCollection(rects, edgecolors='red', facecolors="None", label='ROIs')
ax.add_collection(pc)
return sample
def get_object_centre(self, dcm) -> (int, int):
"""
Find the phantom object within the image and returns its centre col and row value. Note first element in output = col, second = row.
Args:
dcm:
Returns:
centre: (col:int, row:int)
"""
# Shape Detection
try:
logger.debug('Performing phantom shape detection.')
shape_detector = hazenlib.utils.ShapeDetector(arr=dcm.pixel_array)
orientation = hazenlib.utils.get_image_orientation(dcm.ImageOrientationPatient)
if orientation in ['Sagittal', 'Coronal']:
logger.debug('Orientation = sagittal or coronal.')
# orientation is sagittal to patient
try:
(col, row), size, angle = shape_detector.get_shape('rectangle')
except exc.ShapeError as e:
# shape_detector.find_contours()
# shape_detector.detect()
# contour = shape_detector.shapes['rectangle'][1]
# angle, centre, size = cv.minAreaRect(contour)
# print((angle, centre, size))
# im = cv.drawContours(dcm.pixel_array.copy(), [shape_detector.contours[0]], -1, (0, 255, 255), 10)
# plt.imshow(im)
# plt.savefig("rectangles.png")
# print(shape_detector.shapes.keys())
raise e
elif orientation == 'Transverse':
logger.debug('Orientation = transverse.')
try:
col, row, r = shape_detector.get_shape('circle')
except exc.MultipleShapesError:
logger.info('Warning! Found multiple circles in image, will assume largest circle is phantom.')
col, row, r = self.get_largest_circle(shape_detector.shapes['circle'])
else:
raise exc.ShapeError("Unable to identify phantom shape.")
# Threshold Detection
except exc.ShapeError:
logger.info('Shape detection failed. Performing object centre measurement by thresholding.')
_, mask = self.threshold_image(dcm)
row, col = self.get_binary_mask_centre(mask)
return int(col), int(row)
def snr_by_smoothing(self, dcm: pydicom.Dataset, measured_slice_width=None) -> float:
"""
Parameters
----------
dcm
measured_slice_width
report_path
Returns
-------
normalised_snr: float
"""
col, row = self.get_object_centre(dcm=dcm)
noise_img = self.get_noise_image(dcm=dcm)
signal = [np.mean(roi) for roi in self.get_roi_samples(ax=None, dcm=dcm, centre_col=col, centre_row=row)]
noise = [np.std(roi, ddof=1) for roi in self.get_roi_samples(ax=None, dcm=noise_img, centre_col=col, centre_row=row)]
# note no root_2 factor in noise for smoothed subtraction (one image) method, replicating Matlab approach and McCann 2013
snr = np.mean(np.divide(signal, noise))
normalised_snr = snr * self.get_normalised_snr_factor(dcm, measured_slice_width)
if self.report:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(1, 1)
fig.set_size_inches(5, 5)
fig.tight_layout(pad=1)
axes.set_title('smoothed noise image')
axes.imshow(noise_img, cmap='gray', label='smoothed noise image')
axes.scatter(col, row, 10, marker="+", label='centre')
self.get_roi_samples(axes, dcm, col, row)
axes.legend()
img_path = os.path.realpath(os.path.join(
self.report_path, f'{self.img_desc(dcm)}_smoothing.png'))
fig.savefig(img_path)
self.report_files.append(img_path)
return snr, normalised_snr
def get_largest_circle(self, circles):
largest_r = 0
largest_col, largest_row = 0, 0
for circle in circles:
(col, row), r = cv.minEnclosingCircle(circle)
if r > largest_r:
largest_r = r
largest_col, largest_row = col, row
return largest_col, largest_row, largest_r
def snr_by_subtraction(self, dcm1: pydicom.Dataset, dcm2: pydicom.Dataset, measured_slice_width=None) -> float:
"""
Parameters
----------
dcm1
dcm2
measured_slice_width
report_path
Returns
-------
"""
col, row = self.get_object_centre(dcm=dcm1)
difference = np.subtract(dcm1.pixel_array.astype('int'), dcm2.pixel_array.astype('int'))
signal = [np.mean(roi) for roi in self.get_roi_samples(ax=None, dcm=dcm1, centre_col=col, centre_row=row)]
noise = np.divide(
[np.std(roi, ddof=1) for roi in self.get_roi_samples(ax=None, dcm=difference, centre_col=col, centre_row=row)],
np.sqrt(2))
snr = np.mean(np.divide(signal, noise))
normalised_snr = snr * self.get_normalised_snr_factor(dcm1, measured_slice_width)
if self.report:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(1, 1)
fig.set_size_inches(5, 5)
fig.tight_layout(pad=1)
axes.set_title('difference image')
axes.imshow(difference, cmap='gray', label='difference image')
axes.scatter(col, row, 10, marker="+", label='centre')
self.get_roi_samples(axes, dcm1, col, row)
axes.legend()
img_path = os.path.realpath(os.path.join(
self.report_path, f'{self.img_desc(dcm1)}_snr_subtraction.png'))
fig.savefig(img_path)
self.report_files.append(img_path)
return snr, normalised_snr