Work in progress
- Numerical array, provided by the
numpy.ndarray object. In
scikit-image, images are NumPy arrays, which dimensions
correspond to spatial dimensions of the image, and color channels for
color images. See A crash course on NumPy for images.
- iso-valued 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.
- Differences of intensity or color in an image, which make objects
distinguishable. Several functions to manipulate the contrast of an
image are available in
skimage.exposure. See Contrast and exposure.
- float values
- Representation of real numbers, for example as
np.float64. See Image data types and what they mean. Some operations on images
need a float datatype (such as multiplying image values with
exponential prefactors in
filters.gaussian()), so that
images of integer type are often converted to float type internally. Also
see int values.
- 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
- int values
- 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
np.int64 (for large integer values) and
np.uint8 (for small integer values, typically images of labels
with less than 255 labels). See Image data types and what they mean.
- label image
- 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.
connected components of a binary image and returns an image of
labels. Labels are usually contiguous integers, and
segmentation.relabel_sequential() can be used to relabel
arbitrary labels to sequential (contiguous) ones.
- 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
scikit-image, pixels are the individual elements of
numpy arrays (see A crash course on NumPy for images).
Also see voxel.
- 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 labels, where
the pixels of different objects have been attributed different
integer labels. Several segmentation algorithms are available in
- pixel (smallest element of an image) of a