Segmentation Analysis

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.

ReferenceReference

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