The Wolfram Language includes a variety of image segmentation techniques such as clustering, watershed, region growing, and level set as well as a rich set of functions for post-processing and analyzing the result of the segmentation.

Image Preparation

ColorQuantize — reduce the number of distinct colors in an image

FillingTransform — reduce noise to create smooth regions in an image

GradientFilter, RangeFilter — create an edge map from an image

FindThreshold  ▪  Threshold  ▪  ImageClip

Binary Segmentation

Binarize — segmentation by thresholding pixel intensities

MorphologicalBinarize  ▪  LocalAdaptiveBinarize  ▪  RegionBinarize  ▪  ChanVeseBinarize

Segmentation

ArrayComponents — find identical components

MorphologicalComponents — find morphologically connected components

ImageForestingComponents — image segmentation using various methods

ClusteringComponents — segmentation based on cluster analysis

WatershedComponents — segmentation based on watershed methods

GrowCutComponents — segmentation using cellular automata evolution

RemoveBackground — find the foreground of the image

Component Analysis

ComponentMeasurements — shape and color analysis

SelectComponents  ▪  DeleteSmallComponents  ▪  DeleteBorderComponents

Colorize — color every segment differently

HighlightImage — highlight region of interest