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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.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.
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 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. 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.
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.
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.
pixel (smallest element of an image) of a three-dimensional image.