Merge pull request #305 from ImagingDataCommons/v0.23.0dev

Preparation for 0.23.0 release

.github/workflows/run_unit_tests.yml

@@ -15,7 +15,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12"]
         dependencies: [".", "'.[libjpeg]'"]
 
     steps:

docs/seg.rst

@@ -807,39 +807,38 @@ We recommend that if you do this, you specify ``max_fractional_value=1`` to
 clearly communicate that the segmentation is inherently binary in nature.
 
 Why would you want to make this seemingly rather strange choice? Well,
-``"FRACTIONAL"`` SEGs tend to compress *much* better than ``"BINARY"`` ones
-(see next section). Note however, that this is arguably an misuse of the intent
-of the standard, so *caveat emptor*.
+``"FRACTIONAL"`` SEGs tend to compress better than ``"BINARY"`` ones (see next
+section). Note however, that this is arguably an misuse of the intent of the
+standard, so *caveat emptor*. Also note that while this used to be a more
+serious issue it is less serious now that ``"JPEG2000Lossless"`` compression is
+now supported for ``"BINARY"`` segmentations as of highdicom v0.23.0.
 
 Compression
 -----------
 
 The types of pixel compression available in segmentation images depends on the
-segmentation type. Pixels in a ``"BINARY"`` segmentation image are "bit-packed"
-such that 8 pixels are grouped into 1 byte in the stored array. If a given frame
-contains a number of pixels that is not divisible by 8 exactly, a single byte 
+segmentation type.
+
+Pixels in an uncompressed ``"BINARY"`` segmentation image are "bit-packed" such
+that 8 pixels are grouped into 1 byte in the stored array. If a given frame
+contains a number of pixels that is not divisible by 8 exactly, a single byte
 will straddle a frame boundary into the next frame if there is one, or the byte
 will be padded with zeroes of there are no further frames. This means that
-retrieving individual frames from segmentation images in which each frame
-size is not divisible by 8 becomes problematic. No further compression may be
-applied to frames of ``"BINARY"`` segmentation images.
+retrieving individual frames from segmentation images in which each frame size
+is not divisible by 8 becomes problematic. For this reason, as well as for
+space efficiency (sparse segmentations tend to compress very well), we
+therefore strongly recommend using ``"JPEG2000Lossless"`` compression with
+``"BINARY"`` segmentations. This is the only compression method currently
+supported for ``"BINARY"`` segmentations. However, beware that reading these
+single-bit JPEG 2000 images may not be supported by all other tools and
+viewers.
 
 Pixels in ``"FRACTIONAL"`` segmentation images may be compressed using one of
 the lossless compression methods available within DICOM. Currently *highdicom*
 supports the following compressed transfer syntaxes when creating
 ``"FRACTIONAL"`` segmentation images: ``"RLELossless"``,
 ``"JPEG2000Lossless"``, and ``"JPEGLSLossless"``.
 
-Note that there may be advantages to using ``"FRACTIONAL"`` segmentations to
-store segmentation images that are binary in nature (i.e. only taking values 0
-and 1):
-
-- If the segmentation is very simple or sparse, the lossless compression methods
-  available in ``"FRACTIONAL"`` images may be more effective than the
-  "bit-packing" method required by ``"BINARY"`` segmentations.
-- The clear frame boundaries make retrieving individual frames from
-  ``"FRACTIONAL"`` image files possible.
-
 Multiprocessing
 ---------------
 

pyproject.toml

@@ -7,14 +7,15 @@ name = "highdicom"
 dynamic = ["version"]
 description = "High-level DICOM abstractions."
 readme = "README.md"
-requires-python = ">=3.6"
+requires-python = ">=3.10"
 authors = [
     { name = "Markus D. Herrmann" },
 ]
 maintainers = [
     { name = "Markus D. Herrmann" },
     { name = "Christopher P. Bridge" },
 ]
+license = { text = "LICENSE" }
 classifiers = [
     "Development Status :: 4 - Beta",
     "Intended Audience :: Science/Research",
@@ -23,10 +24,6 @@ classifiers = [
     "Operating System :: Microsoft :: Windows",
     "Operating System :: POSIX :: Linux",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.6",
-    "Programming Language :: Python :: 3.7",
-    "Programming Language :: Python :: 3.8",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
@@ -35,16 +32,16 @@ classifiers = [
 ]
 dependencies = [
     "numpy>=1.19",
-    "pillow-jpls>=1.0",
     "pillow>=8.3",
-    "pydicom>=2.3.0,!=2.4.0",
+    "pydicom>=3.0.1",
+    "pyjpegls>=1.0.0",
 ]
 
 [project.optional-dependencies]
 libjpeg = [
-    "pylibjpeg-libjpeg>=1.3",
-    "pylibjpeg-openjpeg>=1.2",
-    "pylibjpeg>=1.4",
+    "pylibjpeg-libjpeg>=2.1",
+    "pylibjpeg-openjpeg>=2.0.0",
+    "pylibjpeg>=2.0",
 ]
 test = [
     "mypy==0.971",

src/highdicom/_module_utils.py

@@ -281,3 +281,25 @@ def does_iod_have_pixel_data(sop_class_uid: str) -> bool:
     return any(
         is_attribute_in_iod(attr, sop_class_uid) for attr in pixel_attrs
     )
+
+
+def is_multiframe_image(dataset: Dataset):
+    """Determine whether an image is a multiframe image.
+    The definition used is whether the IOD allows for multiple frames, not
+    whether this particular instance has more than one frame.
+
+    Parameters
+    ----------
+    dataset: pydicom.Dataset
+        A dataset to check.
+
+    Returns
+    -------
+    bool:
+        Whether the image belongs to a multiframe IOD.
+
+    """
+    return is_attribute_in_iod(
+        'PerFrameFunctionalGroupsSequence',
+        dataset.SOPClassUID,
+    )

src/highdicom/base.py

@@ -139,8 +139,6 @@ def __init__(
                 "Big Endian transfer syntaxes are retired and no longer "
                 "supported by highdicom."
             )
-        self.is_little_endian = True  # backwards compatibility
-        self.is_implicit_VR = transfer_syntax_uid.is_implicit_VR
 
         # Include all File Meta Information required for writing SOP instance
         # to a file in PS3.10 format.
@@ -154,7 +152,6 @@ def __init__(
             '1.2.826.0.1.3680043.9.7433.1.1'
         )
         self.file_meta.ImplementationVersionName = f'highdicom{__version__}'
-        self.fix_meta_info(enforce_standard=True)
         with BytesIO() as fp:
             write_file_meta_info(fp, self.file_meta, enforce_standard=True)
             self.file_meta.FileMetaInformationGroupLength = len(fp.getvalue())

src/highdicom/content.py

@@ -6,11 +6,12 @@
 
 import numpy as np
 from pydicom.dataset import Dataset
+from pydicom import DataElement
 from pydicom.sequence import Sequence as DataElementSequence
 from pydicom.sr.coding import Code
 from pydicom.sr.codedict import codes
 from pydicom.valuerep import DS, format_number_as_ds
-from pydicom._storage_sopclass_uids import SegmentationStorage
+from pydicom.uid import SegmentationStorage
 
 from highdicom.enum import (
     CoordinateSystemNames,
@@ -469,18 +470,27 @@ def __init__(
                     'Position in Pixel Matrix must be specified for '
                     'slide coordinate system.'
                 )
-            col_position, row_position = pixel_matrix_position
             x, y, z = image_position
-            item.XOffsetInSlideCoordinateSystem = DS(x, auto_format=True)
-            item.YOffsetInSlideCoordinateSystem = DS(y, auto_format=True)
-            item.ZOffsetInSlideCoordinateSystem = DS(z, auto_format=True)
+            col_position, row_position = pixel_matrix_position
             if row_position < 0 or col_position < 0:
                 raise ValueError(
                     'Both items in "pixel_matrix_position" must be positive '
                     'integers.'
                 )
-            item.RowPositionInTotalImagePixelMatrix = row_position
-            item.ColumnPositionInTotalImagePixelMatrix = col_position
+
+            # Use hard-coded tags to avoid the keyword dictionary lookup
+            # (this constructor is called a large number of times in large
+            # multiframe images, so some optimization makes sense)
+            x_tag = 0x0040072a  # XOffsetInSlideCoordinateSystem
+            y_tag = 0x0040073a  # YOffsetInSlideCoordinateSystem
+            z_tag = 0x0040074a  # ZOffsetInSlideCoordinateSystem
+            row_tag = 0x0048021f  # RowPositionInTotalImagePixelMatrix
+            column_tag = 0x0048021e  # ColumnPositionInTotalImagePixelMatrix
+            item.add(DataElement(x_tag, 'DS', DS(x, auto_format=True)))
+            item.add(DataElement(y_tag, 'DS', DS(y, auto_format=True)))
+            item.add(DataElement(z_tag, 'DS', DS(z, auto_format=True)))
+            item.add(DataElement(row_tag, 'SL', int(row_position)))
+            item.add(DataElement(column_tag, 'SL', int(col_position)))
         elif coordinate_system == CoordinateSystemNames.PATIENT:
             item.ImagePositionPatient = [
                 DS(ip, auto_format=True) for ip in image_position