preprocess.py 文件源码

def segment_lung_mask(image, fill_lung_structures=True):

    # not actually binary, but 1 and 2. 
    # 0 is treated as background, which we do not want
    binary_image = np.array(image > -320, dtype=np.int8)+1
    labels = measure.label(binary_image)

    # Pick the pixel in the very corner to determine which label is air.
    #   Improvement: Pick multiple background labels from around the patient
    #   More resistant to "trays" on which the patient lays cutting the air 
    #   around the person in half
    background_label = labels[0,0,0]

    #Fill the air around the person
    binary_image[background_label == labels] = 2


    # Method of filling the lung structures (that is superior to something like 
    # morphological closing)
    if fill_lung_structures:
        # For every slice we determine the largest solid structure
        for i, axial_slice in enumerate(binary_image):
            axial_slice = axial_slice - 1
            labeling = measure.label(axial_slice)
            l_max = largest_label_volume(labeling, bg=0)

            if l_max is not None: #This slice contains some lung
                binary_image[i][labeling != l_max] = 1


    binary_image -= 1 #Make the image actual binary
    binary_image = 1-binary_image # Invert it, lungs are now 1

    # Remove other air pockets insided body
    labels = measure.label(binary_image, background=0)
    l_max = largest_label_volume(labels, bg=0)
    if l_max is not None: # There are air pockets
        binary_image[labels != l_max] = 0

    return binary_image