Doc sections on volumes and images

Author: CPBridge

docs/general.rst

@@ -10,4 +10,7 @@ parts of the library.
    :maxdepth: 2
    :caption: Contents:
 
+   image
+   pixel_transforms
+   volume
    coding

docs/image.rst

@@ -0,0 +1,258 @@
+.. _image:
+
+Images
+======
+
+Highdicom's :class:`highdicom.Image` class is a fundamental class that provides
+methods for working with existing DICOM images. It inherits from pydicom's
+``pydicom.Dataset`` class, and therefore you can access individual DICOM
+attributes just like you can for any dataset. However, the
+:class:`highdicom.Image` class also provides further functionality to make it
+easier to access frames with pixel transforms applied and arrange frames based
+on metadata attributes.
+
+Most of highdicom's classes correspond to individual Information Object
+Definitions defined within the DICOM standard, for example `Segmentation
+Image`, `Parametric Map`, or `Comprehensive3DSR`. However this is **not** the
+case for the :class:`highdicom.Image` class, which captures behavior common to
+a large number of different IODs. Any IOD that includes pixel data can be
+loaded into an Image object. This includes both single frame ("legacy") and
+newer multi-frame objects.
+
+Reading Images
+--------------
+
+You can read in an image from a file using the :func:`highdicom.imread()`
+function:
+
+.. code-block:: python
+
+    import highdicom as hd
+
+    # This is a test file in the highdicom git repository
+    im = hd.imread('data/test_files/ct_image.dcm')
+
+Alternatively, you can convert an existing ``pydicom.Dataset`` instance that
+represents an image to a :class:`highdicom.Image` instances using the
+:meth:`highdicom.Image.from_dataset()` method.
+
+.. code-block:: python
+
+    import pydicom
+    import highdicom as hd
+
+    # This is a test file in the highdicom git repository
+    dcm = pydicom.dcmread('data/test_files/ct_image.dcm')
+
+    im = hd.Image.from_dataset(dcm)
+
+:class:`highdicom.Image` instances cannot be created directly using a
+constructor, they must always be created from an existing DICOM object.
+
+Accessing Frames
+----------------
+
+The :class:`highdicom.Image` class has three methods for accessing individual
+frames of the image:
+
+* :meth:`highdicom.Image.get_raw_frame()`: This returns the raw bytes
+  containing the information for a single frame as a Python ``bytes`` object.
+  If the image uses a compressed transfer syntax (such as JPEG or its
+  variants), the compressed bytestream is returned. This method is intended for
+  advanced users and use cases.
+* :meth:`highdicom.Image.get_stored_frame()`: This returns the frame as a NumPy
+  array with minimal processing. The raw bytes are decompressed if necessary
+  and reshaped to form the frame of the correct shape, but no further pixel
+  transforms are applied. These are referred to as "stored values" within the
+  DICOM standard. Note that the pydicom `.pixel_array` property returns stored
+  values for all frames at once.
+* :meth:`highdicom.Image.get_frame()`: In addition to the above, this method
+  applies pixel transforms stored in the file to the stored values before
+  returning them. The transforms applied are configurable through parameters
+  (see :doc:`pixel_transforms` for more details on pixel transforms), but by
+  default any pixel transform found in the dataset except the value-of-interest
+  (VOI) transform is applied. This should be your default way of accessing
+  image frames in most cases, since it will typtically return the pixels as the
+  creator of the object intended them to be understood. By default, the
+  returned frames have datatype `numpy.float64`, but this can be controlled
+  using the `dtype` parameter.
+
+For all methods, the first parameter ``frame_number`` is an integer giving the
+number of the frame, where the first frame has index 1. This one-based indexing
+may be unnatural for Python programming (which generally uses 0-based
+indexing). The reason for this choice is that the DICOM standard numbers frames
+starting at 1, and in particular if a DICOM object contains references to its
+frames, or those of other objects, 1-based frame numbers are used. If you
+prefer to use 0-based indexing, you can specify ``as_index=True``.
+
+.. code-block:: python
+
+    import numpy as np
+    import highdicom as hd
+
+
+    # This is a test file in the highdicom git repository
+    im = hd.imread('data/test_files/ct_image.dcm')
+
+    # Get raw bytes for the first frame
+    first_frame = im.get_raw_frame(1)
+    print(type(first_frame))
+    # <class  'bytes'>
+
+    # Get stored values for the first frame
+    first_frame = im.get_stored_frame(1)
+    print(first_frame.min(), first_frame.max())
+    # 128 2191
+
+    # Get pixels after rescale/slope applied
+    first_frame = im.get_frame(1)
+    print(first_frame.dtype)
+    # float64
+    print(first_frame.min(), first_frame.max())
+    # -896.0 1167.0
+
+    # Specify an integer datatype
+    first_frame = im.get_frame(1, dtype=np.int32)
+    print(first_frame.dtype)
+    # int32
+
+    # Alternative, using 0-based index
+    first_frame = im.get_frame(0, as_index=True)
+
+These three methods process the raw pixel data "lazily" as needed to avoid
+processing unecessary frames. If you know that you are likely to access frames
+multiple times, you can force caching of the stored values by accessing the
+``.pixel_array`` property (inherited from ``pydicom.Dataset``).
+
+Accessing Total Pixel Matrices
+------------------------------
+
+Digital pathology images in DICOM format are typically stored as "tiled"
+images, where frames are arranged in a 2D pattern across a plane to form a
+large "total pixel matrix". For such images, you typically want to work with
+the large 2D total pixel matrix that is formed by correctly arranging the tiles
+into a 2D array rather than 3D arrays of stacked frames. `highdicom` provides
+the :meth:`highdicom.Image.get_total_pixel_matrix()` method for this purpose.
+
+Called without any parameters, it returns a 2D array containing the full total
+pixel matrix. The two dimensions are the spatial dimensions. Behind the scenes
+highdicom has stitched together the required frames stored in the original file
+for you.
+
+.. code-block:: python
+
+    import highdicom as hd
+
+    # Read in a tiled test file from the highdicom repo
+    im = hd.imread('data/test_files/sm_image.dcm')
+
+    # Get the full total pixel matrix
+    tpm = im.get_total_pixel_matrix()
+
+    expected_shape = (
+        im.TotalPixelMatrixRows,
+        im.TotalPixelMatrixColumns,
+        3,  # RGB channels
+    )
+    assert tpm.shape == expected_shape
+
+Furthermore, you can request a sub-region of the full total pixel matrix by
+specifying the start and/or stop indices for the rows and/or columns within the
+total pixel matrix. Note that this method follows DICOM 1-based convention for
+indexing rows and columns, i.e. the first row and column of the total pixel
+matrix are indexed by the number 1 (not 0 as is common within Python). Negative
+indices are also supported to index relative to the last row or column, with -1
+being the index of the last row or column. Like for standard Python indexing,
+the stop indices are specified as one beyond the final row/column in the
+returned array. The requested region does not have to start or stop
+at the edges of the underlying frames: `highdicom` stitches together only the
+relevant parts of the frames to create the requested image for you.
+
+.. code-block:: python
+
+    import highdicom as hd
+
+    # Read in a tiled test file from the highdicom repo
+    im = hd.imread('data/test_files/sm_image.dcm')
+
+    # Get a region of the total pixel matrix
+    tpm = im.get_total_pixel_matrix(
+        row_start=15,
+        row_end=25,
+        column_start=26,
+    )
+
+    expected_shape = (10, 25, 3)
+    assert tpm.shape == expected_shape
+
+Accessing Volumes
+-----------------
+
+Many multi-frame images, especially from radiology modalities such as CT, MRI,
+DBT, and PET, contain frames that can be arranged together to form voxels on a
+regularly-sampled rectangular 3D grid. The :meth:`highdicom.Image.get_volume()`
+method checks for this case and, if possible, returns a 3D voxel array array
+with the affine matrix describing its position in the frame of reference
+coordinate system, as a :class:`highdicom.Volume`. To just check whether it is
+possible to form a volume from the frames, use the
+:class:`highdicim.Image.get_volume_geometry()` method, which will return
+``None`` if no volume can be formed.
+
+.. code-block:: python
+
+    from pydicom.data import get_testdata_file
+
+    import highdicom as hd
+
+    # Load an enhanced (multiframe) CT image
+    im = hd.imread(get_testdata_file('eCT_Supplemental.dcm'))
+
+    geometry = im.get_volume_geometry()
+
+    assert geometry is not None
+
+    vol = im.get_volume()
+    print(vol.spatial_shape)
+    # (2, 512, 512)
+
+    print(vol.affine)
+    # [[   0.          0.         -0.388672   99.5     ]
+    #  [  -0.          0.388672    0.       -301.5     ]
+    #  [  10.          0.          0.       -159.      ]
+    #  [   0.          0.          0.          1.      ]]
+
+Further parameters allow you to access a sub-region of the volume and control
+the pixel transforms applied to the frames.
+
+Any single frame image that defines its position within the frame-of-reference
+coordinate system can accessed as a volume, as can any image with a total pixel
+matrix. In these cases, the first spatial dimension will always have shape 1.
+
+See :doc:`volume` for an overview of the :class:`highdicom.Volume` class.
+
+Lazy Frame Retrieval
+--------------------
+
+The :func:`highdicom.imread()` function provides the ``lazy_frame_retrieval``
+parameter. If used, the metadata is loaded from the file without the pixel
+data. Pixel data is subsequently loaded from the file whenever it is needed by
+one of the :class:`highdicom.Image` object's methods. This can save loaded
+unneeded pixel data from file when only a subset of it is needed.
+
+In this example, lazy frame retrieval is used to avoid loading all frames of a
+tiled image:
+
+.. code-block:: python
+
+    import highdicom as hd
+
+    # Read in a tiled test file from the highdicom repo
+    im = hd.imread(
+        'data/test_files/sm_image.dcm',
+        lazy_frame_retrieval=True
+    )
+
+    # Get a region of the total pixel matrix
+    tpm = im.get_total_pixel_matrix(row_end=20)
+
+Whether this saves time depends on your usage patterns and hardware.

docs/pixel_transforms.rst

@@ -0,0 +1,4 @@
+.. _pixel-transforms:
+
+Pixel Transforms
+================