1. 前言
为了能够在Labelme上对Dicom图像进行编辑，这里对python环境下Dicom文件的读取进行了研究。在Dicom图像中CT的窗宽窗位是一个很重要的概念，但是找了半天在pydicom中没有相关设置函数，这里跟DCMTK还不一样。但是可以根据两个tag得到CT图像的CT值，那就是（0028|1052）：rescale intercept和（0028|1053）：rescale slope。则按照下面的算子得到CT图像，进而就可以调整窗宽窗位了
Hu = pixel * slope + intercept至于那个部位的窗宽窗位是多少各位看官就可以自行百度了。
2. 代码实现
# -*- coding=utf-8 -*-
import matplotlib.pyplot as plt
import pydicom
import pydicom.uid
import sys
import PIL.Image as Image
from PyQt5 import QtGui
import osstep1：读取Dicom图像数据与得到CT值图像（CT图）
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,
]
# 支持的传输语法
def supports_transfer_syntax(dicom_dataset):
"""
Returns
-------
bool
True if this pixel data handler might support this transfer syntax.
False to prevent any attempt to try to use this handler
to decode the given transfer syntax
"""
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
# 加载Dicom图像数据
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.")
# 设置窗宽窗位
#dicom_dataset.
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图像需要得到其CT值图像
pixel_array = pixel_array * dicom_dataset.RescaleSlope + dicom_dataset.RescaleIntercept # 获得图像的CT值
pixel_array = pixel_array.reshape(dicom_dataset.Rows, dicom_dataset.Columns*dicom_dataset.SamplesPerPixel)
return pixel_array, dicom_dataset.Rows, dicom_dataset.Columnsstep2：对于CT图像设置窗宽窗位
# 调整CT图像的窗宽窗位
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 < 0
img_temp[min_index] = 0
max_index = img_temp > 255
img_temp[max_index] = 255
return img_tempstep3：获取Dicom中的tag信息
第一种方式：
# 加载Dicom图片中的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第二种方式
dcm = pydicom.dcmread(fileanme) # 加载Dicom数据
print(dcm[0x0008, 0x0060])
>>(0008, 0060) Modality CS: 'MR'
print(dcm[0x0008, 0x0060].VR)
>>CS
print(dcm[0x0008, 0x0060].value)
>>MRstep4：Dicom图像数据转换为PIL.Image
dcm = pydicom.dcmread(fileanme) # 加载Dicom数据
dcm_img = Image.fromarray(img_data) # 将Numpy转换为PIL.Image
dcm_img = dcm_img.convert('L')
# 保存为jpg文件，用作后面的生成label用
dcm_img.save('temp.jpg')
# 显示图像
dcm_img.show()3. 结果展示
调整了窗宽窗位的脑部CT图像：
