def get_segmented_lungs(im, plot=False):
# Step 1: Convert into a binary image.
binary = im < -400
# Step 2: Remove the blobs connected to the border of the image.
cleared = clear_border(binary)
# Step 3: Label the image.
label_image = label(cleared)
# Step 4: Keep the labels with 2 largest areas.
areas = [r.area for r in regionprops(label_image)]
areas.sort()
if len(areas) > 2:
for region in regionprops(label_image):
if region.area < areas[-2]:
for coordinates in region.coords:
label_image[coordinates[0], coordinates[1]] = 0
binary = label_image > 0
# Step 5: Erosion operation with a disk of radius 2. This operation is seperate the lung nodules attached to the blood vessels.
selem = disk(2)
binary = binary_erosion(binary, selem)
# Step 6: Closure operation with a disk of radius 10. This operation is to keep nodules attached to the lung wall.
selem = disk(10) # CHANGE BACK TO 10
binary = binary_closing(binary, selem)
# Step 7: Fill in the small holes inside the binary mask of lungs.
edges = roberts(binary)
binary = ndi.binary_fill_holes(edges)
# Step 8: Superimpose the binary mask on the input image.
get_high_vals = binary == 0
im[get_high_vals] = -2000
return im, binary
python类roberts()的实例源码
def get_segmented_lungs(im, plot=False):
# Step 1: Convert into a binary image.
binary = im < -400
# Step 2: Remove the blobs connected to the border of the image.
cleared = clear_border(binary)
# Step 3: Label the image.
label_image = label(cleared)
# Step 4: Keep the labels with 2 largest areas.
areas = [r.area for r in regionprops(label_image)]
areas.sort()
if len(areas) > 2:
for region in regionprops(label_image):
if region.area < areas[-2]:
for coordinates in region.coords:
label_image[coordinates[0], coordinates[1]] = 0
binary = label_image > 0
# Step 5: Erosion operation with a disk of radius 2. This operation is seperate the lung nodules attached to the blood vessels.
selem = disk(2)
binary = binary_erosion(binary, selem)
# Step 6: Closure operation with a disk of radius 10. This operation is to keep nodules attached to the lung wall.
selem = disk(10) # CHANGE BACK TO 10
binary = binary_closing(binary, selem)
# Step 7: Fill in the small holes inside the binary mask of lungs.
edges = roberts(binary)
binary = ndi.binary_fill_holes(edges)
# Step 8: Superimpose the binary mask on the input image.
get_high_vals = binary == 0
im[get_high_vals] = -2000
return im, binary
