Glossary#

Work in progress

array#: Numerical array, provided by the 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 A crash course on NumPy for images.
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 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 skimage.exposure. See Contrast and exposure.
disk#: A filled-in circle.
float#: Representation of real numbers, for example as np.float32 or 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.
float values#: See 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 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 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.
int values#: See int.
iso-valued contour#: See 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. measure.label() 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.
label image#: See 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 A crash course on NumPy for images). Also see 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 labels, where the pixels of different objects have been attributed different integer labels. Several segmentation algorithms are available in skimage.segmentation.
voxel#: pixel (smallest element of an image) of a three-dimensional image.