- CT图像的文件格式是dicom格式,可以用pydicom进行处理,其含有许多的DICOM Tag信息。查看一些tag信息的代码实现如下所示。
# __author: Y # date: 2019/12/10 import pydicom import numpy as np import matplotlib import pandas import SimpleITK as sitk import cv2 from PIL import Image # 应用pydicom来提取患者信息 def loadFile(filename): ds = sitk.ReadImage(filename) image_array = sitk.GetArrayFromImage(ds) frame_num, width, height = image_array.shape print('frame_num:%s, width:%s, height:%s'%(frame_num, width, height)) return image_array, frame_num, width, height 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 information['NumberOfFrames'] = ds.NumberOfFrames print(information) return information loadFile('../000000.dcm') loadFileInformation('abdominallymphnodes-26828')
- CT图像是根据人体不同组织器官对X射线的吸收能力不同扫描得到的,由许多轴向切片组成三维图像,从三个方向观察可以分为三个视图,分别是轴状图、冠状图和矢状图。运用pydicom读取dcm格式的CT图像切片的代码实现如下所示。
def load_scan(path): # 获取切片 slices = [pydicom.read_file(path + '/' + s) for s in os.listdir(path)] # 按ImagePositionPatient[2]排序,否则得到的扫描面是混乱无序的 slices.sort(key=lambda x: int(x.ImagePositionPatient[2])) # 获取切片厚度 try: slice_thickness = np.abs(slices[0].ImagePositionPatient[2] - slices[1].ImagePositionPatient[2]) except: slice_thickness = np.abs(slices[0].SliceLocation - slices[1].SliceLocation) for s in slices: s.SliceThickness = slice_thickness return slices
- 为了更好地观察不同器官,需要将像素值转换为CT值,单位为HU。计算方法为HU=pixel*rescale slope+rescale intercept。其中,rescale slope和rescale intercept是dicom图像文件的两个tag信息。代码实现如下所示
def get_pixels_hu(slices): image = np.stack([s.pixel_array for s in slices]) # Convert to int16 (from sometimes int16), # should be possible as values should always be low enough (<32k) image = image.astype(np.int16) # image.shape = (666, 512, 512) # Set outside-of-scan pixels to 0 # The intercept is usually -1024, so air is approximately 0 # CT扫描边界之外的灰度值是固定的,为2000,需要把这些值设置为0 image[image == -2000] = 0 # Convert to Hounsfield units (HU) 转换为HU,就是 灰度值*rescaleSlope+rescaleIntercept for slice_number in range(len(slices)): intercept = slices[slice_number].RescaleIntercept slope = slices[slice_number].RescaleSlope if slope != 1: image[slice_number] = slope * image[slice_number].astype(np.float64) image[slice_number] = image[slice_number].astype(np.int16) image[slice_number] += np.int16(intercept) return np.array(image, dtype=np.int16)
- 将像素值转换为CT值之后,可以设置窗宽、窗位来更好地观察不同组织、器官。每种组织都有一定的CT值或CT值范围,如果想观察这一特定组织,就将窗位设置为其对应的CT值,而窗宽是CT图像可以显示的CT值范围,窗位大小是窗宽上、下限的平均值。CT图像将窗宽范围内的CT值划分为16个灰阶进行显示,例如,CT图像范围为80HU,划分为16个灰阶,则80/16=5HU,在CT图像上,只有CT值相差5HU以上的组织才可以观察到。设置窗位、窗宽的代码实现如下所示。
def get_window_size(organ_name): if organ_name == 'lung': # 肺部 ww 1500-2000 wl -450--600 center = -500 width = 2000 elif organ_name == 'abdomen': # 腹部 ww 300-500 wl 30-50 center = 40 width = 500 elif organ_name == 'bone': # 骨窗 ww 1000-1500 wl 250-350 center = 300 width = 2000 elif organ_name == 'lymph': # 淋巴、软组织 ww 300-500 wl 40-60 center = 50 width = 300 elif organ_name == 'mediastinum': # 纵隔 ww 250-350 wl 250-350 center = 40 width = 350 return center, width def setDicomCenWid(slices, organ_name): img = slices center, width = get_window_size(organ_name) min = (2 * center - width) / 2.0 + 0.5 max = (2 * center + width) / 2.0 + 0.5 dFactor = 255.0 / (max - min) d, h, w = np.shape(img) for n in np.arange(d): for i in np.arange(h): for j in np.arange(w): img[n, i, j] = int((img[n, i, j] - min) * dFactor) min_index = img < 0 img[min_index] = 0 max_index = img > 255 img[max_index] = 255 return img
- CT图像不同扫描面的像素尺寸、粗细粒度是不同的,这对进行CNN有不好的影响,因此需要进行重构采样,将图像重采样为[1,1,1]的代码实现如下所示
def resample(image, slice, new_spacing=[1, 1, 1]): spacing = map(float, ([slice.SliceThickness] + [slice.PixelSpacing[0], slice.PixelSpacing[1]])) spacing = np.array(list(spacing)) resize_factor = spacing / new_spacing new_real_shape = image.shape * resize_factor new_shape = np.round(new_real_shape) real_resize_factor = new_shape / image.shape new_spacing = spacing / real_resize_factor image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest') return image, new_spacing
- 为了更好地进行网络训练,通常进行标准化,有min-max标准化和0-1标准化。
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转载自blog.csdn.net/Acmer_future_victor/article/details/106428407
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