Binarization

Algorithms which allow transforming gray level or color images into a binary image.

Thresholding: Transforms a grayscale image into a binary image where all pixels with an initial gray level value lying between two user-defined bounds are set to 1 and all others are set to 0.
ThresholdingByCriterion: Performs a criterion based thresholding on a grayscale image.
AutoThresholdingBright: Computes and applies an automatic threshold on a gray level image to detect bright particles on a dark background.
AutoThresholdingDark: Computes and applies an automatic threshold on a gray level image to detect dark particles on a bright background.
ColorThresholding: Transforms a color image into a binary image.
TopHat: Detects the compact dark or bright areas of a grayscale image.
FeatureAdaptiveThresholding: Thresholds an image depending on two grayscale measurements.
AdaptiveThresholding2d: Performs a binarization of a grayscale image based on the mean intensity of a sliding window centered around each pixel.
AdaptiveThresholding3d: Performs a binarization of a grayscale image based on the mean intensity of a sliding window centered around each voxel.
HysteresisThresholding: Performs a thresholding with a conditional propagation.

Binarization methods are used when the relevant information in the gray level image corresponds to a specific gray level interval. In the binary image, the pixels of interest are set to 1 (objects or foreground), and to 0 for all other pixels (the background).