我们从Python开源项目中，提取了以下2个代码示例，用于说明如何使用skimage.filters.roberts()。
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
