tools.py 文件源码

def segment_lung_mask(image, speedup=4):
    def largest_label_volume(im, bg=-1):
        vals, counts = np.unique(im, return_counts=True)

        counts = counts[vals != bg]
        vals = vals[vals != bg]

        if len(counts) > 0:
            return vals[np.argmax(counts)]
        else:
            return None
    if speedup>1:
        smallImage = transform.downscale_local_mean(image,(1,speedup,speedup));
    else:
        smallImage = image;
    # not actually binary, but 1 and 2. 
    # 0 is treated as background, which we do not want
    binary_image = np.array((smallImage < -320) & (smallImage>-1400), dtype=np.int8)
    #return binary_image;
    for i, axial_slice in enumerate(binary_image):
        axial_slice = 1-axial_slice
        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

    # Remove other air pockets insided body
    labels = measure.label(binary_image, background=0)
    m = labels.shape[0]//2;
    check_layers = labels[m-12:m+20:4,:,:];
    l_max = largest_label_volume(check_layers, bg=0)

    while l_max is not None: # There are air pockets
        idx = np.where(check_layers==l_max);
        ii = np.vstack(idx[1:]).flatten();
        if np.max(ii)>labels.shape[1]-24/speedup or np.min(ii)<24/speedup:
            binary_image[labels==l_max] = 0;
            labels = measure.label(binary_image, background=0)
            m = labels.shape[0]//2;
            check_layers = labels[m-12:m+20:4,:,:];
            l_max = largest_label_volume(check_layers, bg=0)
        else:     
            binary_image[labels != l_max] = 0
            break

    if speedup<=1:
        return binary_image
    else:
        res = np.zeros(image.shape,dtype=np.uint8);
        for i,x in enumerate(binary_image):
            orig = np.copy(x);
            x = binary_dilation(x,disk(5))
            x = binary_erosion(x,disk(5))
            x = np.logical_or(x,orig)            
            y = transform.resize(x*1.0,image.shape[1:3]);
            res[i][y>0.5]=1

        return res;