仅获取肺部的二值图像

Question

我遇到了一个问题，我正在努力制作肺的纯二进制掩模，其中像素值在肺内是1，在肺外是1。我使用了kmeans和otsu以及其他一些方法来分割肺部。我会附上一些例子图片。

First Example

Second example, same patient/CT. I have no idea why this one has a circle around it

这是一个3d numpy数组的链接。它是所有切片中的一个，所以您可能只想尝试一个切片。

https://drive.google.com/file/d/1nktGBYZGz1iJDR_-yarzlRs-c4xOp__9/view?usp=sharing

如你所见，肺被分割得很好。(图片中间是白色的)。有没有办法让我识别中间的白色斑点(肺)，并使其外部的每个像素都变黑(0?)如果有人能给我指路，我将非常感激。

下面是我用来分割肺部的代码(生成一个二进制掩码)：

def HUValueSegmentation(图片，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

Answer 1

由于肺部在蒙版上的一个大的负片区域的中间，我通过对图像中最大的负片区域内的区域进行bitwise_and来过滤掉蒙版的其余部分。

​

​

​

​

编辑:我根本没有改变代码的主体，但我修改了它，将一个numpy数组作为一系列图像。

​

​

import cv2
import numpy as np

# load numpy array
images = np.load("array.npy");

# do the lung thing
counter = 0;
for img in images:
    # convert to uint8
    img *= 255;
    inty = img.astype(np.uint8);

    # dilate
    kernel = np.ones((3,3), np.uint8);
    mask = cv2.dilate(inty, kernel, iterations = 1);

    # invert
    mask = cv2.bitwise_not(mask);

    # contours # OpenCV 3.4, this returns (contours, _) on OpenCV 2 and 4
    _, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE);

    # find biggest
    biggest = None;
    big_size = -1;
    for con in contours:
        area = cv2.contourArea(con);
        if area > big_size:
            big_size = area;
            biggest = con;

    # draw fill mask
    mask2 = np.zeros_like(mask);
    cv2.drawContours(mask2, [biggest], -1, (255), -1);

    # combine
    lungs_mask = cv2.bitwise_and(inty, mask2);

    # show
    cv2.imshow("Lungs", inty);
    cv2.imshow("Mask", lungs_mask);
    cv2.waitKey(30);