仅获取肺部的二进制图像

如何解决仅获取肺部的二进制图像

我有一个问题，我正在努力制作肺部的纯二进制掩码，其中像素值是肺部内部的 1，肺部外部的 1。我使用了 kmeans 和 otsu 以及其他一些方法来分割肺部。我将附上一些示例图片。

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。它包含所有切片，因此您可能只想尝试一个切片。

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

如您所见，肺分割得很好。 （图片中间是白色的）。有什么方法可以让我识别中间的白色斑点（肺）并使它外面的每个像素都变成黑色（0？），如果有人能指导我，我将非常感谢您的帮助。

这是我用来分割肺的代码（制作二进制掩码）：

定义 HUValueSegmentation（图像，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]

#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

解决方法

由于肺部位于面罩上一个大的负区域的中间，我通过对图像中最大负区域内的区域进行 bitwise_and 来过滤掉面罩的其余部分。

编辑：我根本没有更改代码的主体，但我对其进行了修改，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);