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detect_roi_henry.py
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from typing import Tuple
import numpy as np
from skimage import color, img_as_ubyte
from skimage.feature import canny
from skimage.transform import hough_circle, hough_circle_peaks, hough_ellipse
from skimage.draw import circle_perimeter, ellipse_perimeter
from skimage.morphology import dilation, square
from skimage.measure import label
from matplotlib.patches import Rectangle
import scipy.ndimage as ndimage
import SimpleITK as sitk
class RegionOfInterest():
def __init__(self):
pass
@staticmethod
def _mean_roi_centroid(centroids_x: Tuple[int], centroids_y: Tuple[int]) -> Tuple[int]:
centroid_x = int(np.asarray(centroids_x).mean())
centroid_y = int(np.asarray(centroids_y).mean())
return centroid_x, centroid_y
@staticmethod
def detect_roi_sa(sitk_image: sitk.Image,
debug: bool = False) -> Tuple[int]:
image = sitk.GetArrayFromImage(sitk_image)
image = np.swapaxes(image, 0, -1)
all_cx = []
all_cy = []
ed_slice = image[:, :, 0]
es_slice = image[:, :, 10]
width = ed_slice.shape[0]
height = ed_slice.shape[1]
image_size = (width + height) // 2
diff_image = abs(ed_slice - es_slice)
edge_image = canny(diff_image, sigma=2.0, low_threshold=0.8, high_threshold=0.98,
use_quantiles=True)
lower_range = int(image_size * 0.06)
upper_range = int(image_size * 0.08)
hough_radii = np.arange(lower_range, upper_range, 20)
hough_res = hough_circle(edge_image, hough_radii)
accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii,
total_num_peaks=3,
normalize=False)
all_cx.extend(cx)
all_cy.extend(cy)
if debug:
import matplotlib.pyplot as plt
plt.imshow(ed_slice, cmap='gray')
plt.title('Passed End Diastolic Image')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(es_slice, cmap='gray')
plt.title('Passed End Systolic Image')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(diff_image, cmap='magma')
plt.title('Difference between ED and ES')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(edge_image, cmap='cubehelix')
plt.title('Detected Edges on Difference Image')
plt.axis('off')
plt.show()
plt.close()
mean_cx, mean_cy = RegionOfInterest._mean_roi_centroid(cx, cy)
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10, 4))
image = ((ed_slice - ed_slice.min()) *
(1 / (ed_slice.max() - ed_slice.min()) * 255)).astype('uint8')
image = color.gray2rgb(image)
for center_y, center_x, radius in zip(cy, cx, radii):
circy, circx = circle_perimeter(center_y, center_x, radius,
shape=image.shape)
image[circy, circx] = (220, 60, 40)
ax.imshow(image, cmap=plt.cm.gray)
ax.scatter([mean_cx], [mean_cy], marker='x')
plt.title('Detected Hough Circles and Centroid')
plt.axis('off')
plt.show()
plt.close()
mean_cx, mean_cy = RegionOfInterest._mean_roi_centroid(all_cx, all_cy)
return mean_cy, mean_cx
@staticmethod
def detect_roi_la(sitk_image: sitk.Image,
debug: bool = True) -> Tuple[int]:
image = sitk.GetArrayFromImage(sitk_image)
image = np.swapaxes(image, 0, -1)
ed_slice = image[:, :, 0]
es_slice = image[:, :, 10]
diff_image = abs(ed_slice - es_slice)
edge_image = canny(diff_image, sigma=2.0, low_threshold=0.6, high_threshold=0.96,
use_quantiles=True)
# The accuracy corresponds to the bin size of a major axis.
# The value is chosen in order to get a single high accumulator.
# The threshold eliminates low accumulators
# Min_size: Minimal major axis length
# Max_size: Maximal minor axis length
# result = hough_ellipse(edge_image, accuracy=150, threshold=500)
# result.sort(order='accumulator')
# best = list(result[-1])
# yc, xc, a, b = [int(round(x)) for x in best[1:5]]
# orientation = best[5]
# cy, cx = ellipse_perimeter(yc, xc, a, b, orientation)
width = ed_slice.shape[1]
height = ed_slice.shape[0]
image_size = (width + height) // 2
lower_range = int(image_size * 0.1)
upper_range = int(image_size * 0.3)
hough_radii = np.arange(lower_range, upper_range, 3)
hough_res = hough_circle(edge_image, hough_radii)
accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii,
total_num_peaks=10,
normalize=False)
mean_cx, mean_cy = RegionOfInterest._mean_roi_centroid(cx, cy)
if debug:
import matplotlib.pyplot as plt
plt.imshow(ed_slice, cmap='bone')
plt.title('Passed End Diastolic Image')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(es_slice, cmap='bone')
plt.title('Passed End Systolic Image')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(diff_image, cmap='magma')
plt.title('Difference between ED and ES')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(edge_image, cmap='cubehelix')
plt.title('Detected Edges on Difference Image')
plt.axis('off')
plt.show()
plt.close()
mean_cx, mean_cy = RegionOfInterest._mean_roi_centroid(cx, cy)
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10, 4))
ed_slice[ed_slice > 350] = 350
image = ((ed_slice - ed_slice.min()) *
(1 / (ed_slice.max() - ed_slice.min()) * 255)).astype('uint8')
image = color.gray2rgb(image)
for center_y, center_x, radius in zip(cy, cx, radii):
circy, circx = circle_perimeter(center_y, center_x, radius,
shape=image.shape)
image[circy, circx] = (220, 60, 40)
ax.imshow(image, cmap=plt.cm.gray)
ax.scatter([mean_cx], [mean_cy], marker='x')
plt.title('Detected Hough Circles and Centroid')
plt.axis('off')
plt.show()
plt.close()
# image[cy, cx] = (0, 0, 255)
# edges = color.gray2rgb(img_as_ubyte(edge_image))
# edges[cy, cx] = (250, 0, 0)
# fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4),
# sharex=True, sharey=True)
# ax1.set_title('Original picture')
# ax1.imshow(image, cmap = 'bone')
# ax2.set_title('Edge (white) and result (red)')
# ax2.imshow(edges)
# plt.show()
return mean_cx, mean_cy, edge_image
@staticmethod
def detect_roi_dilate_n_crop(image, debug):# sitk_image: sitk.Image,
# debug: bool = True) -> Tuple[int]:
# image = sitk.GetArrayFromImage(sitk_image)
ed_slice = image[:, :, 0]
try:
es_slice = image[:, :, 10]
except:
es_slice = image[:, :, -1]
else:
es_slice = image[:, :, 10]
diff_image = abs(ed_slice - es_slice)
edge_image = canny(diff_image, sigma=2.0, low_threshold=0.6, high_threshold=0.96,
use_quantiles=True)
edge_image_dilated = dilation(edge_image, square(5))
labels = label(edge_image_dilated)
largestCC = labels == np.argmax(np.bincount(labels.flat)[1:]) + 1
temp_top = largestCC.shape[0]
temp_bottom = 0
temp_left = largestCC.shape[1]
temp_right = 0
for (row_index, row) in enumerate(largestCC):
for (col_index, col) in enumerate(row):
if col > 0:
# Get the top-most coordinate
if row_index < temp_top:
temp_top = row_index
# Get the bottom-most coordinate
if row_index > temp_bottom:
temp_bottom = row_index
# Get the left-most coordinate
if col_index < temp_left:
temp_left = col_index
# Get the right-most coordinate
if col_index > temp_right:
temp_right = col_index
box = [temp_left, temp_right, temp_top, temp_bottom]
rect = Rectangle((box[0],box[2]),(box[1]-box[0]),(box[3]-box[2]),linewidth=1,edgecolor='r',facecolor='none')
cropped_image = image[box[2]:box[3], box[0]:box[1]]
if debug:
import matplotlib.pyplot as plt
plt.imshow(ed_slice, cmap='bone')
plt.title('Passed End Diastolic Image')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(es_slice, cmap='bone')
plt.title('Passed End Systolic Image')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(diff_image, cmap='magma')
plt.title('Difference between ED and ES')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(edge_image, cmap='cubehelix')
plt.title('Detected Edges on Difference Image')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(edge_image_dilated, cmap='cubehelix')
plt.title('Dilated Edges Image')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(largestCC, cmap='cubehelix')
ax = plt.gca()
ax.add_patch(rect)
plt.title('Largest Connected Component')
plt.axis('off')
plt.show()
plt.close()
return box
@staticmethod
def detect_roi_dilate_n_crop_2ch(sitk_image: sitk.Image,
debug: bool = True) -> Tuple[int]:
image = sitk.GetArrayFromImage(sitk_image)
image = np.swapaxes(image, 0, -1)
ed_slice = image[:, :, 0]
try:
es_slice = image[:, :, 10]
except:
es_slice = image[:, :, -1]
else:
es_slice = image[:, :, 10]
diff_image = abs(ed_slice - es_slice)
edge_image = canny(diff_image, sigma=2.0, low_threshold=0.6, high_threshold=0.96,
use_quantiles=True)
width = ed_slice.shape[0]
height = ed_slice.shape[1]
image_size = (width + height) // 2
lower_range = int(image_size * 0.06)
upper_range = int(image_size * 0.08)
hough_radii = np.arange(lower_range, upper_range, 20)
hough_res = hough_circle(edge_image, hough_radii)
accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii,
total_num_peaks=3,
normalize=False)
img = ((ed_slice - ed_slice.min()) *
(1 / (ed_slice.max() - ed_slice.min()) * 255)).astype('uint8')
circle_mask = np.zeros(img.shape)
for center_y, center_x, radius in zip(cy, cx, radii):
circy, circx = circle_perimeter(center_y, center_x, radius,
shape=img.shape)
circle_mask[circy, circx] = 1
circle_mask = ndimage.binary_fill_holes(circle_mask)
circle_mask = ~circle_mask
circle_mask = np.array(circle_mask, 'int8')
masked_circle_edge_image = edge_image * circle_mask
edge_image_dilated = dilation(masked_circle_edge_image, square(5))
labels = label(edge_image_dilated)
largestCC = labels == np.argmax(np.bincount(labels.flat)[1:]) + 1
temp_top = largestCC.shape[0]
temp_bottom = 0
temp_left = largestCC.shape[1]
temp_right = 0
for (row_index, row) in enumerate(largestCC):
for (col_index, col) in enumerate(row):
if col > 0:
# Get the top-most coordinate
if row_index < temp_top:
temp_top = row_index
# Get the bottom-most coordinate
if row_index > temp_bottom:
temp_bottom = row_index
# Get the left-most coordinate
if col_index < temp_left:
temp_left = col_index
# Get the right-most coordinate
if col_index > temp_right:
temp_right = col_index
box = [temp_left, temp_right, temp_top, temp_bottom]
rect = Rectangle((box[0],box[2]),(box[1]-box[0]),(box[3]-box[2]),linewidth=1,edgecolor='r',facecolor='none')
cropped_image = image[box[2]:box[3], box[0]:box[1]]
if debug:
import matplotlib.pyplot as plt
plt.imshow(ed_slice, cmap='bone')
plt.title('Passed End Diastolic Image')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(es_slice, cmap='bone')
plt.title('Passed End Systolic Image')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(diff_image, cmap='magma')
plt.title('Difference between ED and ES')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(edge_image, cmap='cubehelix')
plt.title('Detected Edges on Difference Image')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(masked_circle_edge_image, cmap='cubehelix')
plt.title('Aorta Removal Edges Images')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(edge_image_dilated, cmap='cubehelix')
plt.title('Dilated Edges Image')
plt.axis('off')
plt.show()
plt.close()
plt.imshow(largestCC, cmap='cubehelix')
ax = plt.gca()
ax.add_patch(rect)
plt.title('Largest Connected Component')
plt.axis('off')
plt.show()
plt.close()
return box