可视化医学图像CT

 IS_LOCAL = False

    
 import numpy as np
    
 import pandas as pd
    
 from skimage.io import imread
    
 import seaborn as sns
    
 import matplotlib.pyplot as plt
    
 from glob import glob
    
 if(IS_LOCAL):
    
     import pydicom as dicom
    
 else:
    
     import dicom
    
 import os

 if(IS_LOCAL):

    
     PATH="../input/siim-medical-image/"
    
 else:
    
     PATH="../input/"
    
 print(os.listdir(PATH))

 data_df = pd.read_csv(os.path.join(PATH,"overview.csv"))

    
 Hide
    
 In [4]:

    print("CT Medical images -  rows:",data_df.shape[0]," columns:", data_df.shape[1])

    print("Number of TIFF images:", len(os.listdir(os.path.join(PATH,"tiff_images"))))

    tiff_data = pd.DataFrame([{'path': filepath} for filepath in glob(PATH+'tiff_images/*.tif')])

 def process_data(path):

    
     data = pd.DataFrame([{'path': filepath} for filepath in glob(PATH+path)])
    
     data['file'] = data['path'].map(os.path.basename)
    
     data['ID'] = data['file'].map(lambda x: str(x.split('_')[1]))
    
     data['Age'] = data['file'].map(lambda x: int(x.split('_')[3]))
    
     data['Contrast'] = data['file'].map(lambda x: bool(int(x.split('_')[5])))
    
     data['Modality'] = data['file'].map(lambda x: str(x.split('_')[6].split('.')[-2]))
    
     return data

    tiff_data = process_data('tiff_images/*.tif')

    tiff_data.head(10)

    print("Number of DICOM files:", len(os.listdir(PATH+"dicom_dir")))

    dicom_data = process_data('dicom_dir/*.dcm')

    dicom_data.head(10)

 def countplot_comparison(feature):

    
     fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize = (16, 4))
    
     s1 = sns.countplot(data_df[feature], ax=ax1)
    
     s1.set_title("Overview data")
    
     s2 = sns.countplot(tiff_data[feature], ax=ax2)
    
     s2.set_title("Tiff files data")
    
     s3 = sns.countplot(dicom_data[feature], ax=ax3)
    
     s3.set_title("Dicom files data")
    
     plt.show()

 def show_images(data, dim=16, imtype='TIFF'):

    
     img_data = list(data[:dim].T.to_dict().values())
    
     f, ax = plt.subplots(4,4, figsize=(16,20))
    
     for i,data_row in enumerate(img_data):
    
     if(imtype=='TIFF'): 
    
         data_row_img = imread(data_row['path'])
    
     elif(imtype=='DICOM'):
    
         data_row_img = dicom.read_file(data_row['path'])
    
     if(imtype=='TIFF'):
    
         ax[i//4, i%4].matshow(data_row_img,cmap='gray')
    
     elif(imtype=='DICOM'):
    
         ax[i//4, i%4].imshow(data_row_img.pixel_array, cmap=plt.cm.bone) 
    
     ax[i//4, i%4].axis('off')
    
     ax[i//4, i%4].set_title('Modality: {Modality} Age: {Age}\nSlice: {ID} Contrast: {Contrast}'.format(**data_row))
    
     plt.show()

    show_images(tiff_data,16,'TIFF')

 # extract voxel data  
    
 def extract_voxel_data(list_of_dicom_files):  
    
     datasets = [dicom.read_file(f) for f in list_of_dicom_files]  
    
      try:  
    
      voxel_ndarray, ijk_to_xyz = dicom_numpy.combine_slices(datasets)  
    
      except dicom_numpy.DicomImportException as e:  
    
      # invalid DICOM data  
    
      raise  
    
      return voxel_ndarray

 dicom_file_path = list(dicom_data[:1].T.to_dict().values())[0]['path']

    
 dicom_file_dataset = dicom.read_file(dicom_file_path)
    
 dicom_file_dataset

 def show_dicom_images(data):

    
     img_data = list(data[:16].T.to_dict().values())
    
     f, ax = plt.subplots(4,4, figsize=(16,20))
    
     for i,data_row in enumerate(img_data):
    
  
    
     data_row_img = dicom.read_file(data_row['path'])
    
     modality = data_row_img.Modality
    
     age = data_row_img.PatientAge
    
     
    
     ax[i//4, i%4].imshow(data_row_img.pixel_array, cmap=plt.cm.bone) 
    
     ax[i//4, i%4].axis('off')
    
     ax[i//4, i%4].set_title('Modality: {} Age: {}\nSlice: {} Contrast: {}'.format(
    
      modality, age, data_row['ID'], data_row['Contrast']))
    
     plt.show()