使用pydicom实现Dicom文件读取与CT图像窗宽窗位调整

灰太狼 2022-05-26 04:45 943阅读 0赞

# 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 os

## step1：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.Columns

## step2：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_temp

## step3：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)
    >>MR

## step4：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：  
![这里写图片描述][70]

# 4. 参考资料 #

1.  [Pydicom User Guide][]
2.  [【医学影像】窗宽窗位与其处理方法][Link 1]

[70]: /images/20220526/a8c16081aba4443d8f20f23d7db5b406.png
[Pydicom User Guide]: https://pydicom.github.io/pydicom/stable/pydicom_user_guide.html
[Link 1]: http://blog.163.com/qimo601@126/blog/static/15822093201361211411428/