从分割图像中去除白色边框
Remove white borders from segmented images
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这样的白色边界：
分段肺（输出）：
未分段肺（输入）：
完整代码可以在 Google Colab Notebook 中找到。 code.
数据集的样本是here。
对于这个问题，我不建议使用Kmeans颜色量化，因为这种技术通常用于有多种颜色并且您想将它们分割成主色块的情况。看看这个previous answer for a typical use case. Since your CT scan images are grayscale, Kmeans would not perform very well. Here's a potential solution using simple image processing with OpenCV:
获取二值图像。 Load input image, convert to grayscale, Otsu's threshold, and find contours.
创建一个空白蒙版来提取所需的对象。我们可以使用np.zeros()创建一个与输入图像大小相同的空蒙版.
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区域阈值内以及 aspect ratio. We use cv2.contourArea(), cv2.arcLength(), and cv2.approxPolyDP() for contour perimeter and contour shape approximation. If we have have found our lung object, we utilize cv2.drawContours() 用白色填充我们的掩码以表示我们要提取的对象。
原始图像的按位与掩码。 最后我们将掩码转换为灰度并按位与 cv2.bitwise_and() 以获得我们的结果。
这是我们逐步可视化的图像处理管道：
灰度->大津的阈值
检测到的要提取的对象以绿色突出显示->已填充掩码
按位并得到我们的结果->可选的白色背景结果
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)
解决这个问题的一种更简单的方法是使用形态侵蚀。只是你必须调整阈值
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这样的白色边界：
分段肺（输出）：
未分段肺（输入）：
完整代码可以在 Google Colab Notebook 中找到。 code.
数据集的样本是here。
对于这个问题，我不建议使用Kmeans颜色量化，因为这种技术通常用于有多种颜色并且您想将它们分割成主色块的情况。看看这个previous answer for a typical use case. Since your CT scan images are grayscale, Kmeans would not perform very well. Here's a potential solution using simple image processing with OpenCV:
获取二值图像。 Load input image, convert to grayscale, Otsu's threshold, and find contours.
创建一个空白蒙版来提取所需的对象。我们可以使用
np.zeros()创建一个与输入图像大小相同的空蒙版.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区域阈值内以及 aspect ratio. We use
cv2.contourArea(),cv2.arcLength(), andcv2.approxPolyDP()for contour perimeter and contour shape approximation. If we have have found our lung object, we utilizecv2.drawContours()用白色填充我们的掩码以表示我们要提取的对象。原始图像的按位与掩码。 最后我们将掩码转换为灰度并按位与
cv2.bitwise_and()以获得我们的结果。
这是我们逐步可视化的图像处理管道：
灰度->大津的阈值
检测到的要提取的对象以绿色突出显示->已填充掩码
按位并得到我们的结果->可选的白色背景结果
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)
解决这个问题的一种更简单的方法是使用形态侵蚀。只是你必须调整阈值