从分割图像中删除白色边框

Question

我正在尝试使用Kmeans通过以下代码分割肺部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')

问题是分割的肺仍然包含如下白色边界：

分段肺（输出）：

未分段肺（输入）：

完整的代码可以在GoogleColab笔记本中找到code

数据集的样本是here

Answer 1

对于这个问题，我不建议使用Kmeans颜色量化，因为这种技术通常只适用于存在各种颜色的情况，并且您希望将它们分割为主色块。看看这个previous answer的典型用例。由于您的CT扫描图像是灰度的，Kmeans的性能不太好。下面是一个使用OpenCV的简单图像处理的潜在解决方案：

1. 获取二进制图像。Load input image，转换为grayscale、Otsu's threshold和find contours

2. 创建空白遮罩以提取所需对象。我们可以使用^{}创建与输入图像大小相同的空掩码

3. 使用轮廓面积和纵横比过滤轮廓。我们通过确保轮廓在指定的区域阈值内以及aspect ratio来搜索肺部对象。我们使用^{}、^{}和^{}进行轮廓周长和轮廓形状近似。如果我们已经找到了我们的lung对象，我们将利用^{}用白色填充遮罩，以表示我们要提取的对象

4. 按位和原始图像掩码。最后，我们将掩码转换为灰度、按位和^{}以获得结果

---

下面是我们逐步可视化的图像处理管道：

灰度->大津阈值

检测到要提取的对象以绿色->填充遮罩突出显示

按位and获得结果->可选结果，背景为白色

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)

Answer 2

解决这个问题的一个更简单的方法是使用形态侵蚀。只是你需要调整阈值