GlossaryΒΆ
Work in progress
array
Numerical array, provided by the {class}`numpy.ndarray` object. In
``scikit-image``, images are NumPy arrays with dimensions that
correspond to spatial dimensions of the image, and color channels for
color images. See {ref}`numpy`.
channel
Typically used to refer to a single color channel in a color image. RGBA
images have an additional alpha (transparency) channel. Functions use a
``channel_axis`` argument to specify which axis of an array corresponds
to channels. Images without channels are indicated via
``channel_axis=None``. Aside from the functions in ``skimage.color``, most
functions with a ``channel_axis`` argument just apply the same operation
across each channel. In this case, the "channels" do not strictly need to
represent color or alpha information, but may be any generic batch
dimension over which to operate.
circle
The perimeter of a {term}`disk`.
contour
Curve along which a 2-D image has a constant value. The interior
(resp. exterior) of the contour has values greater (resp. smaller)
than the contour value.
contrast
Differences of intensity or color in an image, which make objects
distinguishable. Several functions to manipulate the contrast of an
image are available in {mod}`skimage.exposure`. See {ref}`exposure`.
disk
A filled-in {term}`circle`.
float
Representation of real numbers, for example as {obj}`np.float32` or
{obj}`np.float64`. See {ref}`data_types`. Some operations on images
need a float datatype (such as multiplying image values with
exponential prefactors in {func}`filters.gaussian`), so that
images of integer type are often converted to float type internally. Also
see {term}`int` values.
float values
See {term}`float`.
histogram
For an image, histogram of intensity values, where the range of
intensity values is divided into bins and the histogram counts how
many pixel values fall in each bin. See
{func}`exposure.histogram`.
int
Representation of integer numbers, which can be signed or not, and
encoded on one, two, four or eight bytes according to the maximum value
which needs to be represented. In ``scikit-image``, the most common
integer types are {obj}`np.int64` (for large integer values) and
{obj}`np.uint8` (for small integer values, typically images of labels
with less than 255 labels). See {ref}`data_types`.
int values
See {term}`int`.
iso-valued contour
See {term}`contour`.
labels
An image of labels is of integer type, where pixels with the same
integer value belong to the same object. For example, the result of a
segmentation is an image of labels. {func}`measure.label` labels
connected components of a binary image and returns an image of
labels. Labels are usually contiguous integers, and
{func}`segmentation.relabel_sequential` can be used to relabel
arbitrary labels to sequential (contiguous) ones.
label image
See {term}`labels`.
pixel
Smallest element of an image. An image is a grid of pixels, and the
intensity of each pixel is variable. A pixel can have a single
intensity value in grayscale images, or several channels for color
images. In ``scikit-image``, pixels are the individual elements of
``numpy arrays`` (see {ref}`numpy`).
Also see {term}`voxel`.
segmentation
Partitioning an image into multiple objects (segments), for
example an object of interest and its background. The output of a
segmentation is typically an image of {term}`labels`, where
the pixels of different objects have been attributed different
integer labels. Several segmentation algorithms are available in
{mod}`skimage.segmentation`.
voxel
{term}`pixel` (smallest element of an image) of a
three-dimensional image.