• Author / Uploaded
• Dong Hai Nguyen

• Categories
• Procesamiento de señal multidimensional
• Gráficos de computadora
• Imagen
• Áreas de informática
• Gestión de datos

Citation preview

DICOM Image Pre-processing

July 8, 2019

[1]: import numpy as np import pydicom import os import matplotlib.pyplot as plt from glob import glob from IPython.display import Image [2]: def show_dcm_info(dataset): print("Filename.........:", file_path) print("Storage type.....:", dataset.SOPClassUID) print() pat_name = dataset.PatientName display_name = pat_name.family_name + ", " + pat_name.given_name print("Patient's name......:", display_name) print("Patient id..........:", dataset.PatientID) print("Patient's Age.......:", dataset.PatientAge) print("Patient's Sex.......:", dataset.PatientSex) print("Modality............:", dataset.Modality) print("Body Part Examined..:", dataset.BodyPartExamined) print("View Position.......:", dataset.ViewPosition) if 'PixelData' in dataset: rows = int(dataset.Rows) cols = int(dataset.Columns) print("Image size.......: {rows:d} x {cols:d}, {size:d} bytes".format( rows=rows, cols=cols, size=len(dataset.PixelData))) if 'PixelSpacing' in dataset: print("Pixel spacing....:", dataset.PixelSpacing) [3]: def plot_pixel_array(dataset, figsize=(10,10)): plt.figure(figsize=figsize) plt.imshow(dataset.pixel_array, cmap=plt.cm.bone) # python -m pip install pillow # pip install glob3 plt.show()

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[4]: file_path="" data_path = "C: ,→\\Users\\dongh\\Documents\\siim-acr-pneumothorax-segmentation\\sample_images" output_path = working_path = "C:\\Users\\dongh\\" g = glob(data_path + '/*.dcm') # Print out the first 5 file names to verify we're in the right folder. print ("Total of %d DICOM images.\nFirst 5 filenames:" % len(g)) print ('\n'.join(g[:5])) Total of 1 DICOM images. First 5 filenames: C:\Users\dongh\Documents\siim-acr-pneumothorax-segmentation\sample_images\1.2.27 6.0.7230010.3.1.4.8323329.12743.1517875241.599591.dcm [5]: # # Loop over the image files and store everything into a list. # def load_scan(path): slices = [pydicom.read_file(path + '/' + s,force=True) for s in os. ,→listdir(path)] slices.sort(key = lambda x: int(x.InstanceNumber)) print (len(slices))

try: slice_thickness = np.abs(slices[0].PixelSpacing[0] - slices[0]. ,→PixelSpacing[1]) except: slice_thickness = np.abs(slices[0].PixelSpacing[1] - slices[0]. ,→PixelSpacing[2]) for s in slices: s.SliceThickness = slice_thickness return slices

def get_pixels_hu(scans): image = np.stack([s.pixel_array for s in scans]) # Convert to int16 (from sometimes int16), # should be possible as values should always be low enough (