删除分割图像中的白色边框

Question

我正在尝试使用以下代码使用 Kmeans 分割肺部 CT 图像：

def process_mask(mask):
    convex_mask = np.copy(mask)
    for i_layer in range(convex_mask.shape[0]):
        mask1  = np.ascontiguousarray(mask[i_layer])
        if np.sum(mask1)>0:
            mask2 = convex_hull_image(mask1)
            if np.sum(mask2)>2*np.sum(mask1):
                mask2 = mask1
        else:
            mask2 = mask1
        convex_mask[i_layer] = mask2
    struct = generate_binary_structure(3,1)
    dilatedMask = binary_dilation(convex_mask,structure=struct,iterations=10)

    return dilatedMask

def lumTrans(img):
    lungwin = np.array([-1200.,600.])
    newimg = (img-lungwin[0])/(lungwin[1]-lungwin[0])
    newimg[newimg<0]=0
    newimg[newimg>1]=1
    newimg = (newimg*255).astype('uint8')
    return newimg


def lungSeg(imgs_to_process,output,name):

    if os.path.exists(output+'/'+name+'_clean.npy') : return
    imgs_to_process = Image.open(imgs_to_process)
    
    img_to_save = imgs_to_process.copy()
    img_to_save = np.asarray(img_to_save).astype('uint8')

    imgs_to_process = lumTrans(imgs_to_process)    
    imgs_to_process = np.expand_dims(imgs_to_process, axis=0)
    x,y,z = imgs_to_process.shape 
  
    img_array = imgs_to_process.copy()  
    A1 = int(y/(512./100))
    A2 = int(y/(512./400))

    A3 = int(y/(512./475))
    A4 = int(y/(512./40))
    A5 = int(y/(512./470))
    for i in range(len(imgs_to_process)):
        img = imgs_to_process[i]
        print(img.shape)
        x,y = img.shape
        #Standardize the pixel values
        allmean = np.mean(img)
        allstd = np.std(img)
        img = img-allmean
        img = img/allstd
        # Find the average pixel value near the lungs
        # to renormalize washed out images
        middle = img[A1:A2,A1:A2] 
        mean = np.mean(middle)  
        max = np.max(img)
        min = np.min(img)
        
        kmeans = KMeans(n_clusters=2).fit(np.reshape(middle,[np.prod(middle.shape),1]))
        centers = sorted(kmeans.cluster_centers_.flatten())
        threshold = np.mean(centers)
        thresh_img = np.where(img<threshold,1.0,0.0)  # threshold the image
       
        eroded = morphology.erosion(thresh_img,np.ones([4,4]))
        dilation = morphology.dilation(eroded,np.ones([10,10]))
        
        labels = measure.label(dilation)
        label_vals = np.unique(labels)
        regions = measure.regionprops(labels)
        good_labels = []
        for prop in regions:
            B = prop.bbox
            if B[2]-B[0]<A3 and B[3]-B[1]<A3 and B[0]>A4 and B[2]<A5:
                good_labels.append(prop.label)
        mask = np.ndarray([x,y],dtype=np.int8)
        mask[:] = 0
       
        for N in good_labels:
            mask = mask + np.where(labels==N,1,0)
        mask = morphology.dilation(mask,np.ones([10,10])) # one last dilation
        imgs_to_process[i] = mask

    m1 = imgs_to_process
    
    convex_mask = m1
    dm1 = process_mask(m1)
    dilatedMask = dm1
    Mask = m1
    extramask = dilatedMask ^ Mask
    bone_thresh = 180
    pad_value = 0

    img_array[np.isnan(img_array)]=-2000
    sliceim = img_array
    sliceim = sliceim*dilatedMask+pad_value*(1-dilatedMask).astype('uint8')
    bones = sliceim*extramask>bone_thresh
    sliceim[bones] = pad_value


    x,y,z = sliceim.shape
    if not os.path.exists(output): 
        os.makedirs(output)
    
    img_to_save[sliceim.squeeze()==0] = 0
    
    im = Image.fromarray(img_to_save)

    im.save(output + name + '.png', 'PNG')

问题是分段的肺仍然包含白色边框，如下所示：

分段肺（输出）：

未分段的肺（输入）：

完整的代码可以在 Google Colab Notebook 中找到。代码。

数据集的样本在这里。

Answer 1

对于这个问题，我不建议使用 Kmeans 颜色量化，因为该技术通常保留用于存在多种颜色并且您希望将它们分割成主色块的情况。查看之前的典型用例答案。由于您的 CT 扫描图像是灰度图像，Kmeans 的性能不会很好。这是使用OpenCV进行简单图像处理的潜在解决方案：

1. 获取二值图像。 加载输入图像，转换为灰度、Otsu 阈值，并查找轮廓。

2. 创建一个空白蒙版以提取所需的对象。我们可以用来np.zeros()创建一个与输入图像大小相同的空蒙版。

3. 使用轮廓面积和纵横比过滤轮廓。我们通过确保轮廓位于指定的区域阈值和纵横比内来搜索肺部对象。我们使用cv2.contourArea()、cv2.arcLength()、 和cv2.approxPolyDP()进行轮廓周长和轮廓形状近似。如果我们找到了肺部对象，我们就会用cv2.drawContours()白色填充掩模来代表我们想要提取的对象。

4. 使用原始图像进行按位和掩码。最后，我们将掩模转换为灰度和按位，cv2.bitwise_and()以获得我们的结果。

这是我们的图像处理管道的逐步可视化：

灰度大津->阈值

检测到的要提取的对象以绿色->填充蒙版突​​出显示

按位与得到我们的结果->可选结果以白色背景代替

代码

import cv2
import numpy as np

image = cv2.imread('1.png')
highlight = image.copy()
original = image.copy()

# Convert image to grayscale, Otsu's threshold, and find contours
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]

# Create black mask to extract desired objects
mask = np.zeros(image.shape, dtype=np.uint8)

# Search for objects by filtering using contour area and aspect ratio
for c in contours:
    # Contour area
    area = cv2.contourArea(c)
    # Contour perimeter
    peri = cv2.arcLength(c, True)
    # Contour approximation
    approx = cv2.approxPolyDP(c, 0.035 * peri, True)
    (x, y, w, h) = cv2.boundingRect(approx)
    aspect_ratio = w / float(h)
    # Draw filled contour onto mask if passes filter
    # These are arbitary values, may need to change depending on input image
    if aspect_ratio <= 1.2 or area < 5000:
        cv2.drawContours(highlight, [c], 0, (0,255,0), -1)
        cv2.drawContours(mask, [c], 0, (255,255,255), -1)

# Convert 3-channel mask to grayscale then bitwise-and with original image for result
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
result = cv2.bitwise_and(original, original, mask=mask)

# Uncomment if you want background to be white instead of black
# result[mask==0] = (255,255,255)

# Display
cv2.imshow('gray', gray)
cv2.imshow('thresh', thresh)
cv2.imshow('highlight', highlight)
cv2.imshow('mask', mask)
cv2.imshow('result', result)

# Save images
# cv2.imwrite('gray.png', gray)
# cv2.imwrite('thresh.png', thresh)
# cv2.imwrite('highlight.png', highlight)
# cv2.imwrite('mask.png', mask)
# cv2.imwrite('result.png', result)
cv2.waitKey(0)