Handling of compressed image data¶
How to get image data from compressed DICOM images
Preconditions¶
To be able to decompress compressed DICOM image data, you need to have
one or more packages installed that are able to handle this kind of data.
pydicom
detects the installed packages and provides image data handlers
that use the available packages.
The following packages can be used with pydicom
:
GDCM - this is the package that supports most compressed formats
Pillow, ideally with
jpeg
andjpeg2000
plugins
Note that you always need the NumPy package to be able to handle image data.
Caution
We rely on the image handling capacity of the mentioned packages and cannot guarantee the correctness of the generated uncompressed images. Be sure to verify the correctness of generated images using other means before you use them for medical purposes.
Supported Transfer Syntaxes¶
As far as we have been able to verify, the following transfer syntaxes are handled by the given packages:
Transfer Syntax |
NumPy |
NumPy + JPEG-LS |
NumPy + GDCM |
NumPy + Pillow |
|
---|---|---|---|---|---|
Name |
UID |
||||
Explicit VR Little Endian |
1.2.840.10008.1.2.1 |
✓ |
✓ |
✓ |
✓ |
Implicit VR Little Endian |
1.2.840.10008.1.2 |
✓ |
✓ |
✓ |
✓ |
Explicit VR Big Endian |
1.2.840.10008.1.2.2 |
✓ |
✓ |
✓ |
✓ |
Deflated Explicit VR Little Endian |
1.2.840.10008.1.2.1.99 |
✓ |
✓ |
✓ |
✓ |
RLE Lossless |
1.2.840.10008.1.2.5 |
✓ |
✓ |
✓ |
✓ |
JPEG Baseline (Process 1) |
1.2.840.10008.1.2.4.50 |
✓ |
✓1 |
||
JPEG Extended (Process 2 and 4) |
1.2.840.10008.1.2.4.51 |
✓ |
✓1 |
||
JPEG Lossless (Process 14) |
1.2.840.10008.1.2.4.57 |
✓ |
|||
JPEG Lossless (Process 14, SV1) |
1.2.840.10008.1.2.4.70 |
✓ |
✓ |
||
JPEG LS Lossless |
1.2.840.10008.1.2.4.80 |
✓ |
✓ |
||
JPEG LS Lossy 3 |
1.2.840.10008.1.2.4.81 |
✓ |
✓ |
||
JPEG2000 Lossless |
1.2.840.10008.1.2.4.90 |
✓ |
✓2 |
||
JPEG2000 4 |
1.2.840.10008.1.2.4.91 |
✓5 |
|||
JPEG2000 Multi-component Lossless |
1.2.840.10008.1.2.4.92 |
||||
JPEG2000 Multi-component |
1.2.840.10008.1.2.4.93 |
Usage¶
To use decompressed image data from compressed DICOM images, you have two options:
use
decompress()
on the dataset to convert it in-place and work with the pixel data as described beforeget an uncompressed copy of the pixel data as a NumPy array using
Dataset.pixel_array
without touching the original dataset
Note
Using decompress()
adapts the transfer syntax of the data set, but not the Photometric Interpretation.
The Photometric Interpretation may not match the pixel data, depending on the used decompression handler.