.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_image_processing_plot_downsize_image.py: ================================ Downsize MRI image using pydicom ================================ This example shows how to downsize an MR image from :math:`512 imes 512` to :math:`64 imes 64`. The downsizing is performed by taking the central section instead of averagin the pixels. Finally, the image is store as a dicom image. .. note:: This example requires the Numpy library to manipulate the pixel data. .. rst-class:: sphx-glr-script-out Out: .. code-block:: none The image has 64 x 64 voxels The downsampled image has 8 x 8 voxels The information of the data set after downsampling: (0008, 0008) Image Type CS: ['DERIVED', 'SECONDARY', 'OTHER'] (0008, 0012) Instance Creation Date DA: '20040826' (0008, 0013) Instance Creation Time TM: '185434' (0008, 0014) Instance Creator UID UI: 1.3.6.1.4.1.5962.3 (0008, 0016) SOP Class UID UI: MR Image Storage (0008, 0018) SOP Instance UID UI: 1.3.6.1.4.1.5962.1.1.4.1.1.20040826185059.5457 (0008, 0020) Study Date DA: '20040826' (0008, 0021) Series Date DA: '' (0008, 0022) Acquisition Date DA: '' (0008, 0030) Study Time TM: '185059' (0008, 0031) Series Time TM: '' (0008, 0032) Acquisition Time TM: '' (0008, 0050) Accession Number SH: '' (0008, 0060) Modality CS: 'MR' (0008, 0070) Manufacturer LO: 'TOSHIBA_MEC' (0008, 0080) Institution Name LO: 'TOSHIBA' (0008, 0090) Referring Physician's Name PN: '' (0008, 0201) Timezone Offset From UTC SH: '-0400' (0008, 1010) Station Name SH: '000000000' (0008, 1060) Name of Physician(s) Reading Study PN: '----' (0008, 1070) Operators' Name PN: '----' (0008, 1090) Manufacturer's Model Name LO: 'MRT50H1' (0010, 0010) Patient's Name PN: 'CompressedSamples^MR1' (0010, 0020) Patient ID LO: '4MR1' (0010, 0030) Patient's Birth Date DA: '' (0010, 0040) Patient's Sex CS: 'F' (0010, 1020) Patient's Size DS: '' (0010, 1030) Patient's Weight DS: "80.0000" (0018, 0010) Contrast/Bolus Agent LO: '' (0018, 0020) Scanning Sequence CS: 'SE' (0018, 0021) Sequence Variant CS: 'NONE' (0018, 0022) Scan Options CS: '' (0018, 0023) MR Acquisition Type CS: '3D' (0018, 0050) Slice Thickness DS: "0.8000" (0018, 0080) Repetition Time DS: "4000.0000" (0018, 0081) Echo Time DS: "240.0000" (0018, 0083) Number of Averages DS: "1.0000" (0018, 0084) Imaging Frequency DS: "63.92433900" (0018, 0085) Imaged Nucleus SH: 'H' (0018, 0086) Echo Number(s) IS: "1" (0018, 0091) Echo Train Length IS: '' (0018, 1000) Device Serial Number LO: '-0000200' (0018, 1020) Software Version(s) LO: 'V3.51*P25' (0018, 1314) Flip Angle DS: "90" (0018, 5100) Patient Position CS: 'HFS' (0020, 000d) Study Instance UID UI: 1.3.6.1.4.1.5962.1.2.4.20040826185059.5457 (0020, 000e) Series Instance UID UI: 1.3.6.1.4.1.5962.1.3.4.1.20040826185059.5457 (0020, 0010) Study ID SH: '4MR1' (0020, 0011) Series Number IS: "1" (0020, 0012) Acquisition Number IS: "0" (0020, 0013) Instance Number IS: "1" (0020, 0032) Image Position (Patient) DS: ['-83.9063', '-91.2000', '6.6406'] (0020, 0037) Image Orientation (Patient) DS: ['1.0000', '0.0000', '0.0000', '0.0000', '1.0000', '0.0000'] (0020, 0052) Frame of Reference UID UI: 1.3.6.1.4.1.5962.1.4.4.1.20040826185059.5457 (0020, 0060) Laterality CS: '' (0020, 1040) Position Reference Indicator LO: '' (0020, 1041) Slice Location DS: "0.0000" (0020, 4000) Image Comments LT: 'Uncompressed' (0028, 0002) Samples per Pixel US: 1 (0028, 0004) Photometric Interpretation CS: 'MONOCHROME2' (0028, 0010) Rows US: 8 (0028, 0011) Columns US: 8 (0028, 0030) Pixel Spacing DS: ['0.3125', '0.3125'] (0028, 0100) Bits Allocated US: 16 (0028, 0101) Bits Stored US: 16 (0028, 0102) High Bit US: 15 (0028, 0103) Pixel Representation US: 1 (0028, 0106) Smallest Image Pixel Value SS: 0 (0028, 0107) Largest Image Pixel Value SS: 4000 (0028, 1050) Window Center DS: "600" (0028, 1051) Window Width DS: "1600" (7fe0, 0010) Pixel Data OW: Array of 128 elements (fffc, fffc) Data Set Trailing Padding OB: Array of 126 elements | .. code-block:: default # authors : Guillaume Lemaitre # license : MIT import pydicom from pydicom.data import get_testdata_files print(__doc__) # FIXME: add a full-sized MR image in the testing data filename = get_testdata_files('MR_small.dcm')[0] ds = pydicom.dcmread(filename) # get the pixel information into a numpy array data = ds.pixel_array print('The image has {} x {} voxels'.format(data.shape[0], data.shape[1])) data_downsampling = data[::8, ::8] print('The downsampled image has {} x {} voxels'.format( data_downsampling.shape[0], data_downsampling.shape[1])) # copy the data back to the original data set ds.PixelData = data_downsampling.tobytes() # update the information regarding the shape of the data array ds.Rows, ds.Columns = data_downsampling.shape # print the image information given in the dataset print('The information of the data set after downsampling: \n') print(ds) .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.038 seconds) .. _sphx_glr_download_auto_examples_image_processing_plot_downsize_image.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_downsize_image.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_downsize_image.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_