DICOM0001

Imagens na raiz da pasta

 1 import numpy as np
 2 import dicom
 3 import ia636
 4 import os
 5 
 6 
 7 dicom_path = '/awmedia/www/media/p/Simone/Freesurfer/'
 8 FOLDER = 'DICOM0001'
 9 
10 
11 
12 transition_img = np.zeros((3,256,256), dtype = np.uint8)
13 transition_img[0] = 255
14 dataset_path = dicom_path + FOLDER + '/'
15 FILES = sort(os.listdir(dataset_path))
16 for current_file in FILES:
17     if  current_file.startswith("IM_"):
18         dataset = dicom.read_file(dataset_path + '/' + current_file)
19         temp = str( dataset[(0x2005, 0x140f)].value)
20         temp = temp.splitlines()[6].split()[-1]
21         if temp != "'DIFFUSION'":
22             data = dataset.pixel_array
23             data = ia636.ianormalize(data).astype(np.uint8)
24             i = current_file.split('_')[-1]
25             i = int(i)
26             adshow(data, "Img %d, acquisition contrast: %s" %(i,temp))

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Pasta: 00000001

 1 SUBFOLDER = '00000001'
 2 
 3 dataset_path = dicom_path + FOLDER + '/' + SUBFOLDER + '/'
 4 FILES = sort(os.listdir(dataset_path))
 5 for current_file in FILES:
 6     if  current_file.startswith("IM_"):
 7         dataset = dicom.read_file(dataset_path + '/' + current_file)
 8         temp = str( dataset[(0x2005, 0x140f)].value)
 9         temp = temp.splitlines()[6].split()[-1]
10         if temp != "'DIFFUSION'":
11             data = dataset.pixel_array
12             data = ia636.ianormalize(data).astype(np.uint8)
13             i = current_file.split('_')[-1]
14             i = int(i)
15             adshow(data, "Img %d, acquisition contrast: %s" %(i,temp))

Pasta: 00000002

 1 SUBFOLDER = '00000002'
 2 
 3 dataset_path = dicom_path + FOLDER + '/' + SUBFOLDER + '/'
 4 FILES = sort(os.listdir(dataset_path))
 5 for current_file in FILES:
 6     if  current_file.startswith("IM_"):
 7         dataset = dicom.read_file(dataset_path + '/' + current_file)
 8         temp = str( dataset[(0x2005, 0x140f)].value)
 9         temp = temp.splitlines()[6].split()[-1]
10         if temp != "'DIFFUSION'":
11             data = dataset.pixel_array
12             data = ia636.ianormalize(data).astype(np.uint8)
13             i = current_file.split('_')[-1]
14             i = int(i)
15             adshow(data, "Img %d, acquisition contrast: %s" %(i,temp))

00000003

 1 SUBFOLDER = '00000003'
 2 
 3 dataset_path = dicom_path + FOLDER + '/' + SUBFOLDER + '/'
 4 FILES = sort(os.listdir(dataset_path))
 5 for current_file in FILES:
 6     if  current_file.startswith("IM_"):
 7         dataset = dicom.read_file(dataset_path + '/' + current_file)
 8         temp = str( dataset[(0x2005, 0x140f)].value)
 9         temp = temp.splitlines()[6].split()[-1]
10         if temp != "'DIFFUSION'":
11             data = dataset.pixel_array
12             data = ia636.ianormalize(data).astype(np.uint8)
13             i = current_file.split('_')[-1]
14             i = int(i)
15             adshow(data, "Img %d, acquisition contrast: %s" %(i,temp))

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Pasta: 00000004

 1 SUBFOLDER = '00000004'
 2 
 3 dataset_path = dicom_path + FOLDER + '/' + SUBFOLDER + '/'
 4 FILES = sort(os.listdir(dataset_path))
 5 for current_file in FILES:
 6     if  current_file.startswith("IM_"):
 7         dataset = dicom.read_file(dataset_path + '/' + current_file)
 8         temp = str( dataset[(0x2005, 0x140f)].value)
 9         temp = temp.splitlines()[6].split()[-1]
10         if temp != "'DIFFUSION'":
11             data = dataset.pixel_array
12             data = ia636.ianormalize(data).astype(np.uint8)
13             i = current_file.split('_')[-1]
14             i = int(i)
15             adshow(data, "Img %d, acquisition contrast: %s" %(i,temp))

Img 1, acquisition contrast: 'T1'

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Pasta:00000005

 1 SUBFOLDER = '00000005'
 2 
 3 dataset_path = dicom_path + FOLDER + '/' + SUBFOLDER + '/'
 4 FILES = sort(os.listdir(dataset_path))
 5 for current_file in FILES:
 6     if  current_file.startswith("IM_"):
 7         dataset = dicom.read_file(dataset_path + '/' + current_file)
 8         temp = str( dataset[(0x2005, 0x140f)].value)
 9         temp = temp.splitlines()[6].split()[-1]
10         if temp != "'DIFFUSION'":
11             data = dataset.pixel_array
12             data = ia636.ianormalize(data).astype(np.uint8)
13             i = current_file.split('_')[-1]
14             i = int(i)
15             adshow(data, "Img %d, acquisition contrast: %s" %(i,temp))