前沿

在處理醫(yī)學(xué)圖像時，常常會遇到以Dicom格式保存的醫(yī)學(xué)圖像，如CT、MRI等。Dicom文件是需要專門的軟件或者通過編程，應(yīng)用相應(yīng)的庫進(jìn)行處理。為了能夠更好地服務(wù)下游任務(wù)，例如分割或檢測腹腔CT圖像中某個病灶組織，需要先將Dicom圖像進(jìn)行讀取，脫敏，調(diào)窗等步驟，以便于后續(xù)的編輯。本文使用python對醫(yī)學(xué)Dicom文件進(jìn)行相應(yīng)的處理，相比于封裝好的軟件，筆者認(rèn)為自己動手的可操作性更強。

目錄

1 導(dǎo)入相應(yīng)的包

2 讀取Dicom圖像數(shù)據(jù)

3 設(shè)置CT圖像的窗寬和窗位

4 獲取Dicom圖像的tag信息

5 結(jié)果保存及可視化

導(dǎo)入相應(yīng)的包

# load necessary packages

import matplotlib.pyplot as plt

import pydicom.uid

import sys

from PyQt5 import QtGui

import os

import pydicom

import glob

from PIL import *

import matplotlib.pyplot as plt

from pylab import *

from tkinter.filedialog import *

import PIL.Image as Image

其核心是使用了python中的pydicom庫來處理dicom文件。

讀取Dicom圖像數(shù)據(jù)

have_numpy = True

try:

import numpy

except ImportError:

have_numpy = False

raise

sys_is_little_endian = (sys.byteorder == 'little')

NumpySupportedTransferSyntaxes = [

pydicom.uid.ExplicitVRLittleEndian,

pydicom.uid.ImplicitVRLittleEndian,

pydicom.uid.DeflatedExplicitVRLittleEndian,

pydicom.uid.ExplicitVRBigEndian,

]

# 支持"傳輸"語法

def supports_transfer_syntax(dicom_dataset):

return (dicom_dataset.file_meta.TransferSyntaxUID in

NumpySupportedTransferSyntaxes)

def needs_to_convert_to_RGB(dicom_dataset):

return False

def should_change_PhotometricInterpretation_to_RGB(dicom_dataset):

return False

# 加載 Dicom圖像

def get_pixeldata(dicom_dataset):

"""If NumPy is available, return an ndarray of the Pixel Data.

Raises

------

TypeError

If there is no Pixel Data or not a supported data type.

ImportError

If NumPy isn't found

NotImplementedError

if the transfer syntax is not supported

AttributeError

if the decoded amount of data does not match the expected amount

Returns

-------

numpy.ndarray

The contents of the Pixel Data element (7FE0,0010) as an ndarray.

"""

if (dicom_dataset.file_meta.TransferSyntaxUID not in

NumpySupportedTransferSyntaxes):

raise NotImplementedError("Pixel Data is compressed in a "

"format pydicom does not yet handle. "

"Cannot return array. Pydicom might "

"be able to convert the pixel data "

"using GDCM if it is installed.")

if not have_numpy:

msg = ("The Numpy package is required to use pixel_array, and "

"numpy could not be imported.")

raise ImportError(msg)

if 'PixelData' not in dicom_dataset:

raise TypeError("No pixel data found in this dataset.")

# Make NumPy format code, e.g. "uint16", "int32" etc

# from two pieces of info:

# dicom_dataset.PixelRepresentation -- 0 for unsigned, 1 for signed;

# dicom_dataset.BitsAllocated -- 8, 16, or 32

if dicom_dataset.BitsAllocated == 1:

# single bits are used for representation of binary data

format_str = 'uint8'

elif dicom_dataset.PixelRepresentation == 0:

format_str = 'uint{}'.format(dicom_dataset.BitsAllocated)

elif dicom_dataset.PixelRepresentation == 1:

format_str = 'int{}'.format(dicom_dataset.BitsAllocated)

else:

format_str = 'bad_pixel_representation'

try:

numpy_dtype = numpy.dtype(format_str)

except TypeError:

msg = ("Data type not understood by NumPy: "

"format='{}', PixelRepresentation={}, "

"BitsAllocated={}".format(

format_str,

dicom_dataset.PixelRepresentation,

dicom_dataset.BitsAllocated))

raise TypeError(msg)

if dicom_dataset.is_little_endian != sys_is_little_endian:

numpy_dtype = numpy_dtype.newbyteorder('S')

pixel_bytearray = dicom_dataset.PixelData

if dicom_dataset.BitsAllocated == 1:

# if single bits are used for binary representation, a uint8 array

# has to be converted to a binary-valued array (that is 8 times bigger)

try:

pixel_array = numpy.unpackbits(

numpy.frombuffer(pixel_bytearray, dtype='uint8'))

except NotImplementedError:

# PyPy2 does not implement numpy.unpackbits

raise NotImplementedError(

'Cannot handle BitsAllocated == 1 on this platform')

else:

pixel_array = numpy.frombuffer(pixel_bytearray, dtype=numpy_dtype)

length_of_pixel_array = pixel_array.nbytes

expected_length = dicom_dataset.Rows * dicom_dataset.Columns

if ('NumberOfFrames' in dicom_dataset and

dicom_dataset.NumberOfFrames > 1):

expected_length *= dicom_dataset.NumberOfFrames

if ('SamplesPerPixel' in dicom_dataset and

dicom_dataset.SamplesPerPixel > 1):

expected_length *= dicom_dataset.SamplesPerPixel

if dicom_dataset.BitsAllocated > 8:

expected_length *= (dicom_dataset.BitsAllocated // 8)

padded_length = expected_length

if expected_length & 1:

padded_length += 1

if length_of_pixel_array != padded_length:

raise AttributeError(

"Amount of pixel data %d does not "

"match the expected data %d" %

(length_of_pixel_array, padded_length))

if expected_length != padded_length:

pixel_array = pixel_array[:expected_length]

if should_change_PhotometricInterpretation_to_RGB(dicom_dataset):

dicom_dataset.PhotometricInterpretation = "RGB"

if dicom_dataset.Modality.lower().find('ct') >= 0: # CT圖像需要得到其CT值圖像

pixel_array = pixel_array * dicom_dataset.RescaleSlope + dicom_dataset.RescaleIntercept # 獲得圖像的CT值

pixel_array = pixel_array.reshape(dicom_dataset.Rows, dicom_dataset.Columns*dicom_dataset.SamplesPerPixel)

return pixel_array, dicom_dataset.Rows, dicom_dataset.Columns

讀取到dicom文件中的數(shù)據(jù)后，實質(zhì)上是幾個圖像矩陣，這個過程同時也處理了“脫敏”問題。

設(shè)置CT圖像的窗寬和窗位

def setDicomWinWidthWinCenter(img_data, winwidth, wincenter, rows, cols):

img_temp = img_data

img_temp.flags.writeable = True

min = (2 * wincenter - winwidth) / 2.0 + 0.5

max = (2 * wincenter + winwidth) / 2.0 + 0.5

dFactor = 255.0 / (max - min)

for i in numpy.arange(rows):

for j in numpy.arange(cols):

img_temp[i, j] = int((img_temp[i, j]-min)*dFactor)

min_index = img_temp < min

img_temp[min_index] = 0

max_index = img_temp > max

img_temp[max_index] = 255

return img_temp

該函數(shù)的輸入變量winwidth和wincenter即為需要設(shè)置的窗寬和窗位，這兩個值根據(jù)研究的問題(不同的組織器官對應(yīng)不同的窗寬和窗位，有時候也要根據(jù)圖像效果進(jìn)行一定的調(diào)整)調(diào)整不同的值。網(wǎng)上有很多關(guān)于相關(guān)的窗位和窗寬對應(yīng)值，這里給出一些參考資料，如果遇到不確定的，最好借鑒查閱相應(yīng)領(lǐng)域的論文。Windowing (CT) | Radiology Reference Article | Radiopaedia.org?radiopaedia.org

獲取Dicom圖像的tag信息

def loadFileInformation(filename):

information = {}

ds = pydicom.read_file(filename)

information['PatientID'] = ds.PatientID

information['PatientName'] = ds.PatientName

information['PatientBirthDate'] = ds.PatientBirthDate

information['PatientSex'] = ds.PatientSex

information['StudyID'] = ds.StudyID

information['StudyDate'] = ds.StudyDate

information['StudyTime'] = ds.StudyTime

information['InstitutionName'] = ds.InstitutionName

information['Manufacturer'] = ds.Manufacturer

print(dir(ds))

print(type(information))

return information

這個步驟視需求而定，如果不需要查看dicom文件的具體tag信息，此步驟可以跳過。

結(jié)果保存及可視化

可以單張保存，或者批量處理。

讀取單張dicom文件

def main_single():

dcm = dicom.read_file('81228816') # load dicom_file

# 得到 CT 值，圖像的 長， 寬

pixel_array, dcm.Rows, dcm.Columns = get_pixeldata(dcm)

# 調(diào)整窗位、窗寬

img_data = pixel_array

winwidth = 500

wincenter = 50

rows = dcm.Rows

cols = dcm.Columns

dcm_temp = setDicomWinWidthWinCenter(img_data, winwidth, wincenter, rows, cols)

# 可視化

dcm_img = Image.fromarray(dcm_temp) # 將Numpy轉(zhuǎn)換為PIL.Image

dcm_img = dcm_img.convert('L')

# plt.imshow(img, cmap=plt.cm.bone)

# 保存為jpg文件，用作后面的生成label用

dcm_img.save('../output/temp.jpg')

# 顯示圖像

dcm_img.show()

同時讀取一個文件夾中的 dicom 文件，并處理保存 (寫成循環(huán)即可)

def main_mulit(path):

names = os.listdir(path) # 讀取文件夾中的所有文件名

for i in range(len(names)):

dicom_name = path+names[i]

dcm = pydicom.read_file(dicom_name) # 讀取 dicom 文件

pixel_array, dcm.Rows, dcm.Columns = get_pixeldata(dcm) # 得到 dicom文件的 CT 值

img_data = pixel_array

winwidth = 500

wincenter = 50

rows = dcm.Rows

cols = dcm.Columns

dcm_temp = setDicomWinWidthWinCenter(img_data, winwidth, wincenter, rows, cols) # 調(diào)整窗位、窗寬

# 可視化

dcm_img = Image.fromarray(dcm_temp) # 將 Numpy轉(zhuǎn)換為 PIL.Image

dcm_img = dcm_img.convert('L')

# 批量保存

dcm_img.save("C:/output/%s_%s.png" % (path1, names[i]))

注意，以上都寫成函數(shù)的形式，運行時需要調(diào)用，并注意文件路徑的修改。

單張dicom文件處理

main_single()

批量處理

main_mulit(path)

公眾號文章鏈接：圖像識別 | 使用Python對醫(yī)學(xué)Dicom文件的預(yù)處理(含代碼)?mp.weixin.qq.com

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